diff --git a/README.md b/README.md
index b1200bbdc..2357d661d 100644
--- a/README.md
+++ b/README.md
@@ -21,14 +21,14 @@ Read detailed usage below, or jump to the documentation with [Getting Started](h
 uv pip install "nemo-safe-synthesizer[cu129,engine]" \
   --index https://flashinfer.ai/whl/cu129 \
   --index https://download.pytorch.org/whl/cu129 \
-  --index https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129 \
+  --index https://wheels.vllm.ai/ee0da84ab9e04ac7610e28580af62c365e898389/cu129 \
   --index-strategy unsafe-best-match
 
 # With pip:
 pip install "nemo-safe-synthesizer[cu129,engine]" \
   --extra-index-url https://download.pytorch.org/whl/cu129 \
   --extra-index-url https://flashinfer.ai/whl/cu129 \
-  --extra-index-url https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129
+  --extra-index-url https://wheels.vllm.ai/ee0da84ab9e04ac7610e28580af62c365e898389/cu129
 ```
 
 Or install from source:
diff --git a/docs/dev-notes/posts/introducing-nemo-safe-synthesizer.md b/docs/dev-notes/posts/introducing-nemo-safe-synthesizer.md
index 366e4e0c0..c7840a676 100644
--- a/docs/dev-notes/posts/introducing-nemo-safe-synthesizer.md
+++ b/docs/dev-notes/posts/introducing-nemo-safe-synthesizer.md
@@ -87,7 +87,7 @@ Each score maps to concrete remediation guidance in the documentation. The [Prod
 pip install "nemo-safe-synthesizer[cu129,engine]" \
   --extra-index-url https://download.pytorch.org/whl/cu129 \
   --extra-index-url https://flashinfer.ai/whl/cu129 \
-  --extra-index-url https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129
+  --extra-index-url https://wheels.vllm.ai/ee0da84ab9e04ac7610e28580af62c365e898389/cu129
 ```
 
 The quickest way to run your first pipeline is the CLI:
diff --git a/docs/tutorials/differential-privacy.ipynb b/docs/tutorials/differential-privacy.ipynb
index ad9d9d7a6..759d2f085 100644
--- a/docs/tutorials/differential-privacy.ipynb
+++ b/docs/tutorials/differential-privacy.ipynb
@@ -39,10 +39,10 @@
     "# SPDX-License-Identifier: Apache-2.0\n",
     "\n",
     "if command -v uv > /dev/null 2>&1; then\n",
-    "    uv pip install \"nemo-safe-synthesizer[engine,cu129]\" --index https://flashinfer.ai/whl/cu129 --index https://download.pytorch.org/whl/cu129 --index https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129 --index-strategy unsafe-best-match\n",
+    "    uv pip install \"nemo-safe-synthesizer[engine,cu129]\" --index https://flashinfer.ai/whl/cu129 --index https://download.pytorch.org/whl/cu129 --index https://wheels.vllm.ai/ee0da84ab9e04ac7610e28580af62c365e898389/cu129 --index-strategy unsafe-best-match\n",
     "    uv pip install kagglehub\n",
     "else\n",
-    "    pip install \"nemo-safe-synthesizer[engine,cu129]\" --extra-index-url https://flashinfer.ai/whl/cu129 --extra-index-url https://download.pytorch.org/whl/cu129 --extra-index-url https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129\n",
+    "    pip install \"nemo-safe-synthesizer[engine,cu129]\" --extra-index-url https://flashinfer.ai/whl/cu129 --extra-index-url https://download.pytorch.org/whl/cu129 --extra-index-url https://wheels.vllm.ai/ee0da84ab9e04ac7610e28580af62c365e898389/cu129\n",
     "    pip install kagglehub\n",
     "fi\n"
    ]
diff --git a/docs/tutorials/safe-synthesizer-101.ipynb b/docs/tutorials/safe-synthesizer-101.ipynb
index 825029248..95f0c5808 100644
--- a/docs/tutorials/safe-synthesizer-101.ipynb
+++ b/docs/tutorials/safe-synthesizer-101.ipynb
@@ -45,10 +45,10 @@
         "# SPDX-License-Identifier: Apache-2.0\n",
         "\n",
         "if command -v uv > /dev/null 2>&1; then\n",
-        "    uv pip install \"nemo-safe-synthesizer[engine,cu129]\" --index https://flashinfer.ai/whl/cu129 --index https://download.pytorch.org/whl/cu129 --index https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129 --index-strategy unsafe-best-match\n",
+        "    uv pip install \"nemo-safe-synthesizer[engine,cu129]\" --index https://flashinfer.ai/whl/cu129 --index https://download.pytorch.org/whl/cu129 --index https://wheels.vllm.ai/ee0da84ab9e04ac7610e28580af62c365e898389/cu129 --index-strategy unsafe-best-match\n",
         "    uv pip install datasets\n",
         "else\n",
-        "    pip install \"nemo-safe-synthesizer[engine,cu129]\" --extra-index-url https://flashinfer.ai/whl/cu129 --extra-index-url https://download.pytorch.org/whl/cu129 --extra-index-url https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129\n",
+        "    pip install \"nemo-safe-synthesizer[engine,cu129]\" --extra-index-url https://flashinfer.ai/whl/cu129 --extra-index-url https://download.pytorch.org/whl/cu129 --extra-index-url https://wheels.vllm.ai/ee0da84ab9e04ac7610e28580af62c365e898389/cu129\n",
         "    pip install datasets\n",
         "fi\n"
       ]
@@ -217,7 +217,7 @@
   ],
   "metadata": {
     "kernelspec": {
-      "display_name": "Python 3",
+      "display_name": ".venv",
       "language": "python",
       "name": "python3"
     },
@@ -231,7 +231,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.9"
+      "version": "3.13.12"
     }
   },
   "nbformat": 4,
diff --git a/docs/tutorials/time-series-financial-transactions.ipynb b/docs/tutorials/time-series-financial-transactions.ipynb
index d21ca73af..ef05ed476 100644
--- a/docs/tutorials/time-series-financial-transactions.ipynb
+++ b/docs/tutorials/time-series-financial-transactions.ipynb
@@ -57,9 +57,9 @@
         "# SPDX-License-Identifier: Apache-2.0\n",
         "\n",
         "if command -v uv > /dev/null 2>&1; then\n",
-        "    uv pip install \"nemo-safe-synthesizer[engine,cu129]\" --index https://flashinfer.ai/whl/cu129 --index https://download.pytorch.org/whl/cu129 --index https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129 --index-strategy unsafe-best-match\n",
+        "    uv pip install \"nemo-safe-synthesizer[engine,cu129]\" --index https://flashinfer.ai/whl/cu129 --index https://download.pytorch.org/whl/cu129 --index https://wheels.vllm.ai/ee0da84ab9e04ac7610e28580af62c365e898389/cu129 --index-strategy unsafe-best-match\n",
         "else\n",
-        "    pip install \"nemo-safe-synthesizer[engine,cu129]\" --extra-index-url https://flashinfer.ai/whl/cu129 --extra-index-url https://download.pytorch.org/whl/cu129 --extra-index-url https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129\n",
+        "    pip install \"nemo-safe-synthesizer[engine,cu129]\" --extra-index-url https://flashinfer.ai/whl/cu129 --extra-index-url https://download.pytorch.org/whl/cu129 --extra-index-url https://wheels.vllm.ai/ee0da84ab9e04ac7610e28580af62c365e898389/cu129\n",
         "fi"
       ]
     },
diff --git a/docs/user-guide/getting-started.md b/docs/user-guide/getting-started.md
index a011e4e6e..1ad6865fd 100644
--- a/docs/user-guide/getting-started.md
+++ b/docs/user-guide/getting-started.md
@@ -37,7 +37,7 @@ indexes outside PyPI. You must pass the extra index URLs shown below.
         pip install "nemo-safe-synthesizer[cu129,engine]" \
           --extra-index-url https://download.pytorch.org/whl/cu129 \
           --extra-index-url https://flashinfer.ai/whl/cu129 \
-          --extra-index-url https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129
+          --extra-index-url https://wheels.vllm.ai/ee0da84ab9e04ac7610e28580af62c365e898389/cu129
         ```
 
     === "uv"
@@ -46,7 +46,7 @@ indexes outside PyPI. You must pass the extra index URLs shown below.
         uv pip install "nemo-safe-synthesizer[cu129,engine]" \
           --index https://flashinfer.ai/whl/cu129 \
           --index https://download.pytorch.org/whl/cu129 \
-          --index https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129 \
+          --index https://wheels.vllm.ai/ee0da84ab9e04ac7610e28580af62c365e898389/cu129 \
           --index-strategy unsafe-best-match
         ```
 
diff --git a/docs/user-guide/troubleshooting.md b/docs/user-guide/troubleshooting.md
index 194b3e9ce..e1f119632 100644
--- a/docs/user-guide/troubleshooting.md
+++ b/docs/user-guide/troubleshooting.md
@@ -71,22 +71,21 @@ configuration reference.
 `uv sync` fails with an error mentioning incompatible `transformers` and
 `vllm` requirements.
 
-Safe Synthesizer requires `transformers>=5.6,<6`. vLLM 0.20.0 accepts
-transformers v5, but excludes several early 5.x releases that are not
-compatible with its runtime. Keep vLLM's exclusions intact and resolve to a
-newer transformers v5 release.
+Safe Synthesizer requires `transformers>=5.12,<5.12.1` with vLLM 0.24.0.
+Keep vLLM's constraints intact so the resolver selects the tested
+Transformers/vLLM pairing.
 
 ```toml
 [project]
 dependencies = [
-  "transformers>=5.6,<6",
-  "vllm==0.20.0",
+  "transformers>=5.12,<5.12.1",
+  "vllm==0.24.0",
 ]
 ```
 
 If you've vendored or copied parts of `pyproject.toml` into another project,
 avoid adding a broad `transformers>=5.0,<6` override for vLLM. That can erase
-vLLM's explicit exclusions and allow incompatible early v5 releases.
+vLLM's explicit constraints and allow incompatible Transformers releases.
 
 ### Slow Tokenizer Warning
 
diff --git a/pyproject.toml b/pyproject.toml
index 58b09c3bd..d1796476c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -113,8 +113,8 @@ engine = [
 cpu = [
   "accelerate>=1.1.0",
   "bitsandbytes>=0.46.1",
-  "flashinfer-python==0.6.8.post1; sys_platform=='linux'",
-  "flashinfer-cubin==0.6.8.post1; sys_platform=='linux'",
+  "flashinfer-python==0.6.13; sys_platform=='linux'",
+  "flashinfer-cubin==0.6.13; sys_platform=='linux'",
   "gliner",
   "kernels>=0.12.1,<0.15",
   "peft>=0.18.0",
@@ -127,19 +127,19 @@ cpu = [
   "torchvision==0.26.0; sys_platform == 'darwin'",
   "torchvision==0.26.0+cpu; sys_platform == 'linux'",
   "torchao==0.17.0",
-  "transformers>=5.6,<6",
+  "transformers==5.12.0",
   "triton>=2.0.0; sys_platform=='linux'",
   "trl>=0.23.0",
-  "vllm==0.20.0; sys_platform=='linux'",
+  "vllm==0.24.0; sys_platform=='linux'",
   "xgrammar>=0.2.0; sys_platform=='linux'",
 ]
 
 cu129 = [
   "accelerate>=1.1.0",
   "bitsandbytes>=0.46.1",
-  "flashinfer-python==0.6.8.post1; sys_platform == 'linux'",
-  "flashinfer-cubin==0.6.8.post1; sys_platform == 'linux'",
-  "flashinfer-jit-cache==0.6.8.post1+cu129; sys_platform == 'linux'",
+  "flashinfer-python==0.6.13; sys_platform == 'linux'",
+  "flashinfer-cubin==0.6.13; sys_platform == 'linux'",
+  "flashinfer-jit-cache==0.6.13+cu129; sys_platform == 'linux'",
   "gliner",
   "kernels>=0.12.1,<0.15",
   "nvidia-cublas-cu12; sys_platform == 'linux'",
@@ -152,10 +152,10 @@ cu129 = [
   "torchaudio==2.11.0+cu129; sys_platform == 'linux'",
   "torchvision==0.26.0+cu129; sys_platform == 'linux'",
   "torchao==0.17.0+cu129; sys_platform == 'linux' and platform_machine == 'x86_64'",
-  "transformers>=5.6,<6",
+  "transformers>=5.12,<5.12.1",
   "triton>=2.0.0; sys_platform == 'linux'",
   "trl>=0.23.0",
-  "vllm==0.20.0+cu129; sys_platform == 'linux'",
+  "vllm==0.24.0+cu129; sys_platform == 'linux'",
   "xgrammar>=0.2.0; sys_platform == 'linux'",
 ]
 
@@ -180,8 +180,8 @@ conflicts = [
 
 
 override-dependencies = [
-    "flashinfer-python==0.6.8.post1; sys_platform != 'darwin'",
-    "flashinfer-cubin==0.6.8.post1; sys_platform != 'darwin'",
+    "flashinfer-python==0.6.13; sys_platform != 'darwin'",
+    "flashinfer-cubin==0.6.13; sys_platform != 'darwin'",
 ]
 
 environments = [
@@ -225,7 +225,7 @@ constraint-dependencies = [
   # here we specifiy the dep's source from which UV should attempt to resolve
   triton = { index = "pytorch-cu129", extra = "cu129" }
   vllm = [
-    { index = "vllm-v0-20-0-cu129", marker = "sys_platform == 'linux'", extra = "cu129" },
+    { index = "vllm-v0-24-0-cu129", marker = "sys_platform == 'linux'", extra = "cu129" },
   ]
 # for torch - we have a "cpu" dep set and "cu129" dep set, specified in the optional dependencies above
   torch = [
@@ -319,8 +319,8 @@ url = "https://flashinfer.ai/whl/cu129"
 explicit = true
 
 [[tool.uv.index]]
-name = "vllm-v0-20-0-cu129"
-url = "https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129"
+name = "vllm-v0-24-0-cu129"
+url = "https://wheels.vllm.ai/ee0da84ab9e04ac7610e28580af62c365e898389/cu129"
 explicit = true
 
 [[tool.uv.index]]
diff --git a/script/slurm/slurm_nss_matrix.sh b/script/slurm/slurm_nss_matrix.sh
index 3d6dc755f..84a50cfe9 100644
--- a/script/slurm/slurm_nss_matrix.sh
+++ b/script/slurm/slurm_nss_matrix.sh
@@ -119,7 +119,7 @@ if [[ -n "${NSS_VERSION:-}" ]]; then
     uv pip install "nemo-safe-synthesizer[cu129,engine]==${NSS_VERSION}" \
         --index https://flashinfer.ai/whl/cu129 \
         --index https://download.pytorch.org/whl/cu129 \
-        --index https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129 \
+        --index https://wheels.vllm.ai/ee0da84ab9e04ac7610e28580af62c365e898389/cu129 \
         --index-strategy unsafe-best-match
     NSS_RUN_CMD="${PYPI_VENV}/bin/safe-synthesizer"
     echo "[NSS SLURM] Using PyPI install: nemo-safe-synthesizer==${NSS_VERSION} on Python ${NSS_PYTHON_VERSION}"
diff --git a/src/nemo_safe_synthesizer/generation/processors.py b/src/nemo_safe_synthesizer/generation/processors.py
index 1811287aa..d76a4a7dc 100644
--- a/src/nemo_safe_synthesizer/generation/processors.py
+++ b/src/nemo_safe_synthesizer/generation/processors.py
@@ -6,7 +6,7 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any
+from typing import Any, Protocol
 
 from ..config import SafeSynthesizerParameters
 from ..config.generate import ValidationParameters
@@ -21,9 +21,6 @@
 from ..llm.metadata import ModelMetadata
 from ..observability import get_logger
 
-if TYPE_CHECKING:
-    from transformers import PreTrainedTokenizerBase
-
 logger = get_logger(__name__)
 
 # Re-export the parsed-record types so existing imports of
@@ -39,6 +36,13 @@
 ]
 
 
+class EncodeOnlyTokenizer(Protocol):
+    """Tokenizer interface needed for per-record token counting."""
+
+    def encode(self, text: str, *args: Any, **kwargs: Any) -> list[int]:
+        """Return token IDs for ``text``."""
+
+
 class Processor(ABC):
     """Abstract class for processing text generation results from the LLM.
 
@@ -55,7 +59,7 @@ def __init__(
         self,
         schema: dict[str, Any],
         config: ValidationParameters,
-        tokenizer: PreTrainedTokenizerBase | None = None,
+        tokenizer: EncodeOnlyTokenizer | None = None,
     ):
         self.schema = schema
         self.config = config
@@ -166,7 +170,7 @@ def __init__(
         time_column: str | None,
         interval_seconds: int | None,
         time_format: str | None,
-        tokenizer: PreTrainedTokenizerBase | None = None,
+        tokenizer: EncodeOnlyTokenizer | None = None,
     ):
         super().__init__(schema=schema, config=config, tokenizer=tokenizer)
         if time_column is None:
@@ -232,7 +236,7 @@ def __init__(
         eos_token: str,
         group_by: str,
         order_by: str | None = None,
-        tokenizer: PreTrainedTokenizerBase | None = None,
+        tokenizer: EncodeOnlyTokenizer | None = None,
     ):
         super().__init__(schema=schema, config=config, tokenizer=tokenizer)
         self.group_by: list[str] = [group_by]
@@ -376,7 +380,7 @@ def create_processor(
     schema: dict[str, Any],
     metadata: ModelMetadata,
     config: SafeSynthesizerParameters,
-    tokenizer: PreTrainedTokenizerBase | None = None,
+    tokenizer: EncodeOnlyTokenizer | None = None,
 ) -> Processor:
     """Create the appropriate record processor for the current pipeline mode.
 
diff --git a/src/nemo_safe_synthesizer/generation/vllm_backend.py b/src/nemo_safe_synthesizer/generation/vllm_backend.py
index 4b82585f9..d9fbc4c39 100644
--- a/src/nemo_safe_synthesizer/generation/vllm_backend.py
+++ b/src/nemo_safe_synthesizer/generation/vllm_backend.py
@@ -15,11 +15,11 @@
 from typing import Any, cast
 
 import torch
-from transformers import PreTrainedTokenizerBase
 from vllm import LLM as vLLM
 from vllm import RequestOutput
-from vllm.config import StructuredOutputsConfig
+from vllm.config import AttentionConfig, StructuredOutputsConfig
 from vllm.distributed import cleanup_dist_env_and_memory
+from vllm.inputs.llm import TokensPrompt
 from vllm.lora.request import LoRARequest
 from vllm.sampling_params import SamplingParams, StructuredOutputsParams
 
@@ -35,7 +35,7 @@
 from ..errors import InternalError, ParameterError
 from ..generation.backend import GeneratorBackend
 from ..generation.batch import Batch
-from ..generation.processors import Processor, TabularDataProcessor, create_processor
+from ..generation.processors import EncodeOnlyTokenizer, Processor, TabularDataProcessor, create_processor
 from ..generation.regex_manager import build_json_based_regex, build_json_structural_tag
 from ..generation.results import GenerateJobResults, GenerationBatches, GenerationStatus
 from ..generation.vllm_observability import (
@@ -76,6 +76,38 @@
 os.environ["VLLM_V1_USE_OUTLINES_CACHE"] = "0"
 
 
+def _build_rope_hf_overrides(model_metadata: ModelMetadata) -> dict[str, Any] | None:
+    """Return vLLM ``hf_overrides`` needed for NSS RoPE context extension."""
+    rope_scaling = model_metadata.rope_scaling
+    if rope_scaling is None or rope_scaling.factor <= 1.0:
+        return None
+
+    rope_parameters = dict(rope_scaling.rope_parameters)
+    rope_type = rope_parameters.get("rope_type", rope_scaling.rope_type)
+    if rope_type == "default":
+        rope_type = "linear"
+
+    rope_parameters.update(
+        {
+            "rope_type": rope_type,
+            "factor": float(rope_scaling.factor),
+            "original_max_position_embeddings": model_metadata.base_max_seq_length,
+            "rope_theta": float(rope_scaling.theta),
+        }
+    )
+
+    # vLLM 0.24 expects only native RoPE config overrides here. The effective
+    # context length still belongs on the top-level ``LLM(max_model_len=...)``.
+    return {
+        "rope_parameters": rope_parameters,
+    }
+
+
+def _tokens_prompt(prompt_token_ids: list[int]) -> TokensPrompt:
+    """Build a vLLM token prompt for pre-tokenized generation."""
+    return TokensPrompt(prompt_token_ids=prompt_token_ids)
+
+
 def _secure_outlines_cache_dir() -> None:
     """Pin ``OUTLINES_CACHE_DIR`` to a per-user path and tighten permissions.
 
@@ -282,7 +314,11 @@ def initialize(self, **kwargs) -> None:
         # vLLM 0.12+ accepts attention_config as a constructor arg (replaces the
         # VLLM_ATTENTION_BACKEND env var used in 0.11.x).
         attn_backend = self.config.generation.attention_backend
-        attention_config = {"backend": attn_backend} if attn_backend not in (None, "auto") else None
+        attention_config = (
+            AttentionConfig(backend=attn_backend)  # ty: ignore[invalid-argument-type] -- vLLM validates backend strings.
+            if attn_backend not in (None, "auto")
+            else None
+        )
 
         max_vram = get_max_vram()
         # note this only works for single GPU setups
@@ -293,16 +329,19 @@ def initialize(self, **kwargs) -> None:
             backend=self.config.generation.structured_generation.backend,
         )
         model_ref = ModelRef.parse(self.config.training.pretrained_model)
+        hf_overrides = _build_rope_hf_overrides(self.model_metadata)
 
         with heartbeat("Model loading", logger_name=__name__, model=self.config.training.pretrained_model):
             self.llm = vLLM(
                 model=model_ref.target(),
                 gpu_memory_utilization=max_vram,
+                max_model_len=self.model_metadata.max_seq_length,
                 enable_lora=True,
                 max_lora_rank=self.config.training.lora_r,
                 structured_outputs_config=structured_outputs_config,
                 attention_config=attention_config,
                 trust_remote_code=model_ref.trust_remote_code,
+                hf_overrides=hf_overrides,
             )
 
         # Cache the engine's *effective* runtime config once at init. Read by
@@ -312,9 +351,7 @@ def initialize(self, **kwargs) -> None:
         # asked for.
         self._engine_runtime_config = probe_engine_runtime_config(self.llm)
 
-        # vLLM's get_tokenizer() returns a wider union than HF's PreTrainedTokenizerBase;
-        # in practice it's always a HF tokenizer subclass, so cast for the processor.
-        tokenizer = cast(PreTrainedTokenizerBase, self.llm.get_tokenizer())
+        tokenizer: EncodeOnlyTokenizer = self.llm.get_tokenizer()
         self.processor = create_processor(
             self.schema,
             self.model_metadata,
@@ -434,7 +471,7 @@ def _get_api_param_mapping(self, resolved_temperature: float) -> dict[str, Any]:
                 x if isinstance(x, list) else [x],
             ),
             "temperature": lambda x: ("temperature", resolved_temperature),
-            "num_beams": lambda x: ("beam_width", x) if x > 1 else (None, None),
+            "num_beams": lambda x: (None, None),
             "early_stopping": lambda x: (None, None),
         }
 
@@ -470,8 +507,8 @@ def prepare_params(self, **kwargs) -> None:
 
         Parses a dictionary of parameters into ``SamplingParams``, applying
         necessary transformations from the Safe Synthesizer API to vLLM's API.
-        ``num_beams`` is mapped to ``beam_width`` only when greater than 1;
-        otherwise it is omitted.
+        ``num_beams`` is omitted because vLLM 0.24 no longer accepts the old
+        ``beam_width`` sampling parameter.
 
         Args:
             **kwargs: Sampling parameters to configure.
@@ -536,15 +573,20 @@ def _generate(
                 result = None
                 match input_ids:
                     case torch.Tensor():
-                        logger.debug("vllm generate: prompt_token_ids (torch.Tensor)")
-                        result = self._gen_method(prompt_token_ids=input_ids.tolist())
+                        token_ids = input_ids.tolist()
+                        logger.debug("vllm generate: token prompts (torch.Tensor)")
+                        if all_equal_type(token_ids, int, flatten_iter=False):
+                            result = self._gen_method(prompts=_tokens_prompt(token_ids))
+                        else:
+                            result = self._gen_method(prompts=[_tokens_prompt(ids) for ids in token_ids])
                     case [[*_inner], *_] if all_equal_type(input_ids, int):  # ty: ignore[invalid-argument-type]
                         assert isinstance(input_ids, list)
-                        logger.debug(f"vllm generate: prompt_token_ids ({len(input_ids)} prompts)")
-                        result = self._gen_method(prompt_token_ids=input_ids)
+                        token_ids_batch = cast(list[list[int]], input_ids)
+                        logger.debug(f"vllm generate: token prompts ({len(input_ids)} prompts)")
+                        result = self._gen_method(prompts=[_tokens_prompt(ids) for ids in token_ids_batch])
                     case [*ids] if all_equal_type(ids, int, flatten_iter=False):
-                        logger.debug("vllm generate: prompt_token_ids (single flat list)")
-                        result = self._gen_method(prompt_token_ids=[ids])
+                        logger.debug("vllm generate: token prompts (single flat list)")
+                        result = self._gen_method(prompts=_tokens_prompt(ids))
                     case None:
                         logger.debug(
                             f"vllm generate: processing {len(prompts) if isinstance(prompts, list) else 1} prompts"
diff --git a/src/nemo_safe_synthesizer/llm/metadata.py b/src/nemo_safe_synthesizer/llm/metadata.py
index 22916b116..45567c844 100644
--- a/src/nemo_safe_synthesizer/llm/metadata.py
+++ b/src/nemo_safe_synthesizer/llm/metadata.py
@@ -7,7 +7,7 @@
 
 import importlib
 from pathlib import Path
-from typing import ClassVar, Literal
+from typing import Any, ClassVar, Literal
 
 from pydantic import (
     BaseModel,
@@ -204,6 +204,11 @@ class RopeScaling(BaseModel):
 
     theta: float = Field(default=10000.0, description="Theta for rope scaling.")
 
+    rope_parameters: dict[str, Any] = Field(
+        default_factory=dict,
+        description="Native Transformers v5 RoPE parameters preserved from the model config.",
+    )
+
     @field_validator("factor", mode="after")
     @classmethod
     def validate_factor(cls, v: float | int | None) -> float | int | None:
@@ -219,8 +224,9 @@ def validate_factor(cls, v: float | int | None) -> float | int | None:
     def from_autoconfig(cls, config: PretrainedConfig, factor: float | int | None = None) -> "RopeScaling":
         """Create a ``RopeScaling`` from a HuggingFace ``PretrainedConfig``.
 
-        Reads the model's native ``rope_theta`` and ``rope_type`` and
-        optionally overrides the scaling ``factor``.
+        Reads the model's native Transformers v5 ``rope_parameters`` and
+        optionally overrides the scaling ``factor``. Falls back to legacy
+        top-level ``rope_theta`` and ``rope_scaling`` fields for older configs.
 
         Args:
             config: A loaded HuggingFace model config.
@@ -229,20 +235,24 @@ def from_autoconfig(cls, config: PretrainedConfig, factor: float | int | None =
         Returns:
             A ``RopeScaling`` populated from the config.
         """
-        # Try to get theta from config (different models use different attribute names)
-        theta = getattr(config, "rope_theta", None) or 10000.0
+        rope_parameters = getattr(config, "rope_parameters", None)
+        rope_parameters = dict(rope_parameters) if isinstance(rope_parameters, dict) else {}
 
-        # Try to get rope_type from config
-        rope_type = getattr(config, "rope_scaling", {})
-        if isinstance(rope_type, dict):
-            rope_type = rope_type.get("rope_type", "default")
-        else:
-            rope_type = "default"
+        legacy_rope_scaling = getattr(config, "rope_scaling", None)
+        if isinstance(legacy_rope_scaling, dict):
+            rope_parameters = {**legacy_rope_scaling, **rope_parameters}
+
+        theta = rope_parameters.get("rope_theta", rope_parameters.get("theta"))
+        if not isinstance(theta, (int, float)):
+            theta = getattr(config, "rope_theta", None) or 10000.0
+
+        rope_type = rope_parameters.get("rope_type", rope_parameters.get("type", "default"))
 
         return cls(
             rope_type=rope_type,
             factor=factor or 1.0,
             theta=theta,
+            rope_parameters=rope_parameters,
         )
 
     def to_hf_dict(self) -> dict | None:
@@ -256,11 +266,15 @@ def to_hf_dict(self) -> dict | None:
         """
         if self.factor == 1.0:
             return None
-        return {
-            "rope_type": self.rope_type,
-            "factor": self.factor,
-            "theta": self.theta,
-        }
+        rope_parameters = dict(self.rope_parameters)
+        rope_parameters.update(
+            {
+                "rope_type": self.rope_type,
+                "factor": self.factor,
+                "theta": self.theta,
+            }
+        )
+        return {key: value for key, value in rope_parameters.items() if key != "rope_theta"}
 
 
 class ModelMetadata(BaseModel):
diff --git a/tests/generation/test_vllm_backend.py b/tests/generation/test_vllm_backend.py
index a76fa6d23..919633b4d 100644
--- a/tests/generation/test_vllm_backend.py
+++ b/tests/generation/test_vllm_backend.py
@@ -4,6 +4,7 @@
 """Unit tests for the VllmBackend class private methods and module-level side effects."""
 
 import os
+from functools import partial
 from unittest.mock import MagicMock, patch
 
 import pytest
@@ -20,7 +21,7 @@
 from nemo_safe_synthesizer.errors import ParameterError
 from nemo_safe_synthesizer.generation.processors import TabularDataProcessor
 from nemo_safe_synthesizer.generation.vllm_observability import GenerationObservability
-from nemo_safe_synthesizer.llm.metadata import ModelMetadata
+from nemo_safe_synthesizer.llm.metadata import ModelMetadata, RopeScaling
 
 
 @pytest.fixture
@@ -43,6 +44,8 @@ def mock_model_metadata(fixture_session_cache_dir):
     # helper's fallback when ``max_tokens_per_example`` is unset).
     # Individual tests override per-prompt return values where needed.
     metadata.max_seq_length = 2048
+    metadata.base_max_seq_length = 2048
+    metadata.rope_scaling = None
     metadata.max_tokens_per_example = None
     metadata.generation_max_tokens_for.return_value = 2048
     return metadata
@@ -393,8 +396,56 @@ def test_initialize_passes_cached_snapshot_target_and_trust_to_vllm(
 
         assert backend.llm is mock_llm
         assert mock_vllm.call_args.kwargs["model"] == str(snapshot)
+        assert mock_vllm.call_args.kwargs["max_model_len"] == mock_model_metadata.max_seq_length
+        assert mock_vllm.call_args.kwargs["hf_overrides"] is None
         assert mock_vllm.call_args.kwargs["trust_remote_code"] is True
 
+    def test_initialize_passes_rope_hf_overrides_for_extended_context(
+        self,
+        base_params,
+        mock_model_metadata,
+        mock_schema,
+        mock_workdir,
+        fixture_cached_nvidia_snapshot,
+    ):
+        """VLLM 0.24 requires config overrides when max_model_len exceeds the base context."""
+        cache_root, snapshot = fixture_cached_nvidia_snapshot
+        base_params.training.pretrained_model = "nvidia/Nemotron-Mini-4B-Instruct"
+        mock_model_metadata.base_max_seq_length = 2048
+        mock_model_metadata.max_seq_length = 4096
+        mock_model_metadata.rope_scaling = RopeScaling(
+            rope_type="linear",
+            factor=2.0,
+            theta=10000.0,
+            rope_parameters={"rope_type": "linear", "rope_theta": 10000.0, "low_freq_factor": 1.0},
+        )
+        backend = create_backend(base_params, mock_model_metadata, mock_schema, mock_workdir)
+        mock_llm = MagicMock()
+        mock_llm.get_tokenizer.return_value = MagicMock()
+
+        with (
+            patch(
+                "nemo_safe_synthesizer.generation.vllm_backend.ModelRef._default_hf_cache_root",
+                return_value=cache_root,
+            ),
+            patch("nemo_safe_synthesizer.generation.vllm_backend.vLLM", return_value=mock_llm) as mock_vllm,
+            patch("nemo_safe_synthesizer.generation.vllm_backend.get_max_vram", return_value={0: 0.8}),
+            patch("nemo_safe_synthesizer.generation.vllm_backend.create_processor", return_value=MagicMock()),
+        ):
+            backend.initialize()
+
+        assert mock_vllm.call_args.kwargs["model"] == str(snapshot)
+        assert mock_vllm.call_args.kwargs["max_model_len"] == 4096
+        assert mock_vllm.call_args.kwargs["hf_overrides"] == {
+            "rope_parameters": {
+                "rope_type": "linear",
+                "factor": 2.0,
+                "low_freq_factor": 1.0,
+                "original_max_position_embeddings": 2048,
+                "rope_theta": 10000.0,
+            },
+        }
+
     def test_initialize_caches_engine_runtime_config(
         self,
         base_params,
@@ -623,17 +674,15 @@ def test_temperature_uses_resolved_value(self, base_params, mock_model_metadata,
         assert key == "temperature"
         assert value == 0.8  # Uses resolved, not input
 
-    def test_num_beams_greater_than_one_maps_to_beam_width(
-        self, base_params, mock_model_metadata, mock_schema, mock_workdir
-    ):
-        """Test that num_beams > 1 maps to beam_width."""
+    def test_num_beams_greater_than_one_is_omitted(self, base_params, mock_model_metadata, mock_schema, mock_workdir):
+        """Test that num_beams is omitted because vLLM 0.24 removed beam_width."""
         backend = create_backend(base_params, mock_model_metadata, mock_schema, mock_workdir)
 
         mapping = backend._get_api_param_mapping(resolved_temperature=0.5)
         key, value = mapping["num_beams"](4)
 
-        assert key == "beam_width"
-        assert value == 4
+        assert key is None
+        assert value is None
 
     def test_num_beams_one_returns_none(self, base_params, mock_model_metadata, mock_schema, mock_workdir):
         """Test that num_beams == 1 returns (None, None) to exclude from params."""
@@ -732,6 +781,44 @@ def test_empty_kwargs_returns_empty_dict(self, base_params, mock_model_metadata,
         assert result == {}
 
 
+class TestGenerateDispatch:
+    """Tests for vLLM generation dispatch."""
+
+    def test_generate_passes_flat_token_ids_through_prompts(
+        self, base_params, mock_model_metadata, mock_schema, mock_workdir
+    ):
+        """Flat token IDs use the vLLM 0.24 ``prompts=`` token prompt API."""
+        backend = create_backend(base_params, mock_model_metadata, mock_schema, mock_workdir)
+        captured = {}
+
+        def fake_generate(**kwargs):
+            captured.update(kwargs)
+            return ["ok"]
+
+        backend._gen_method = partial(fake_generate, sampling_params=object())
+
+        assert backend._generate(input_ids=[1, 2, 3]) == ["ok"]
+        assert captured["prompts"] == {"prompt_token_ids": [1, 2, 3]}
+        assert "prompt_token_ids" not in captured
+
+    def test_generate_passes_batched_token_ids_through_prompts(
+        self, base_params, mock_model_metadata, mock_schema, mock_workdir
+    ):
+        """Batched token IDs use one token prompt per batch element."""
+        backend = create_backend(base_params, mock_model_metadata, mock_schema, mock_workdir)
+        captured = {}
+
+        def fake_generate(**kwargs):
+            captured.update(kwargs)
+            return ["ok"]
+
+        backend._gen_method = partial(fake_generate, sampling_params=object())
+
+        assert backend._generate(input_ids=[[1, 2], [3, 4]]) == ["ok"]
+        assert captured["prompts"] == [{"prompt_token_ids": [1, 2]}, {"prompt_token_ids": [3, 4]}]
+        assert "prompt_token_ids" not in captured
+
+
 class TestNoopRemoteCacheBackend:
     """Tests for the _NoopRemoteCacheBackend and conditional installation logic."""
 
diff --git a/tests/generation/test_vllm_shutdown.py b/tests/generation/test_vllm_shutdown.py
index 9a9d40c5a..a7d88d850 100644
--- a/tests/generation/test_vllm_shutdown.py
+++ b/tests/generation/test_vllm_shutdown.py
@@ -29,6 +29,7 @@ def backend(_mock_vllm_cleanup, fixture_session_cache_dir):
     mock_metadata.instruction = "Generate"
     mock_metadata.prompt_config = MagicMock()
     mock_metadata.prompt_config.template = "{instruction} {schema}"
+    mock_metadata.rope_scaling = None
 
     mock_config = MagicMock()
     # Pin branching fields so create_processor() selects TabularDataProcessor deterministically.
diff --git a/tests/llm/test_metadata.py b/tests/llm/test_metadata.py
index cc0de5d38..afaaa82e2 100644
--- a/tests/llm/test_metadata.py
+++ b/tests/llm/test_metadata.py
@@ -19,7 +19,7 @@
 from unittest.mock import MagicMock, patch
 
 from pydantic import ValidationError
-from transformers import PretrainedConfig, PreTrainedTokenizerBase
+from transformers import PretrainedConfig, PreTrainedTokenizerBase, Qwen2Config
 
 from nemo_safe_synthesizer.cli.artifact_structure import Workdir
 from nemo_safe_synthesizer.defaults import (
@@ -457,6 +457,30 @@ def test_resolve_rope_scaling(self, rope_scaling_scenario: RopeScalingScenario,
             assert result.factor == rope_scaling_scenario.expected_factor
             assert result.theta == rope_scaling_scenario.expected_theta
 
+    def test_qwen2_config_preserves_transformers5_rope_parameters(self):
+        """Qwen2 stores native theta in ``rope_parameters`` instead of ``rope_theta``."""
+        config = Qwen2Config()
+        config.rope_parameters = {
+            "rope_type": "default",
+            "rope_theta": 1_000_000.0,
+            "low_freq_factor": 1.0,
+            "high_freq_factor": 4.0,
+        }
+
+        result = resolve_rope_scaling_factor(2.0, autoconfig=config)
+
+        assert result == RopeScaling(
+            rope_type="default",
+            factor=2.0,
+            theta=1_000_000.0,
+            rope_parameters={
+                "rope_type": "default",
+                "rope_theta": 1_000_000.0,
+                "low_freq_factor": 1.0,
+                "high_freq_factor": 4.0,
+            },
+        )
+
 
 class TestModelMetadata:
     """Tests for the ModelMetadata class."""
@@ -681,6 +705,45 @@ def test_metadata_max_tokens_per_example_round_trips_through_metadata_json(self,
         # prompt_len=0 reproduces the old prompt-agnostic budget for round-trip parity.
         assert reloaded.generation_max_tokens_for(0) == int(1500 * GENERATION_MAX_TOKENS_SAFETY_MULTIPLIER)
 
+    def test_qwen2_rope_parameters_round_trip_through_metadata_json(self, sample_prompt_config, sample_workdir):
+        """Native Qwen2 RoPE parameters survive the saved artifact metadata."""
+        autoconfig = Qwen2Config()
+        autoconfig.max_position_embeddings = 2048
+        autoconfig.rope_parameters = {
+            "rope_type": "default",
+            "rope_theta": 1_000_000.0,
+            "low_freq_factor": 1.0,
+            "high_freq_factor": 4.0,
+        }
+        metadata = ModelMetadata(
+            model_name_or_path="Qwen/Qwen2-0.5B",
+            prompt_config=sample_prompt_config,
+            autoconfig=autoconfig,
+            workdir=sample_workdir,
+            rope_scaling=2.0,  # ty: ignore[invalid-argument-type] -- validator accepts numeric factors.
+        )
+
+        metadata.save_metadata()
+
+        with patch("nemo_safe_synthesizer.llm.metadata.AutoConfig") as mock_ac:
+            mock_ac.from_pretrained.return_value = autoconfig
+            reloaded = ModelMetadata.from_metadata_json(
+                sample_workdir.train.adapter.metadata,
+                workdir=sample_workdir,
+            )
+
+        assert reloaded.rope_scaling == RopeScaling(
+            rope_type="default",
+            factor=2.0,
+            theta=1_000_000.0,
+            rope_parameters={
+                "rope_type": "default",
+                "rope_theta": 1_000_000.0,
+                "low_freq_factor": 1.0,
+                "high_freq_factor": 4.0,
+            },
+        )
+
     @patch("nemo_safe_synthesizer.llm.metadata.AutoConfig")
     @patch("nemo_safe_synthesizer.llm.metadata.load_json")
     def test_metadata_legacy_json_without_max_tokens_per_example_loads(
diff --git a/uv.lock b/uv.lock
index dd0cb11ee..7cfbcd1f7 100644
--- a/uv.lock
+++ b/uv.lock
@@ -61,8 +61,8 @@ constraints = [
     { name = "urllib3", specifier = ">=2.7.0" },
 ]
 overrides = [
-    { name = "flashinfer-cubin", marker = "sys_platform != 'darwin'", specifier = "==0.6.8.post1" },
-    { name = "flashinfer-python", marker = "sys_platform != 'darwin'", specifier = "==0.6.8.post1" },
+    { name = "flashinfer-cubin", marker = "sys_platform != 'darwin'", specifier = "==0.6.13" },
+    { name = "flashinfer-python", marker = "sys_platform != 'darwin'", specifier = "==0.6.13" },
 ]
 
 [[package]]
@@ -639,7 +639,7 @@ wheels = [
 
 [[package]]
 name = "compressed-tensors"
-version = "0.15.0.1"
+version = "0.17.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "loguru", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
@@ -648,9 +648,9 @@ dependencies = [
     { name = "torch", version = "2.11.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-21-nemo-safe-synthesizer-cpu' and extra == 'extra-21-nemo-safe-synthesizer-cu129') or (platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-21-nemo-safe-synthesizer-cu129') or (platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-21-nemo-safe-synthesizer-cu129') or (sys_platform != 'linux' and extra == 'extra-21-nemo-safe-synthesizer-cpu' and extra == 'extra-21-nemo-safe-synthesizer-cu129')" },
     { name = "transformers", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/41/1b/c3c4a98ec5f2727656336f07a0c35862195c310d8eb0b2fa5b4be6848680/compressed_tensors-0.15.0.1.tar.gz", hash = "sha256:a8e93054e8a5ec49c980b09ed36c4c1249b4a8ee167920a8e461c4da26e78d99", size = 229412, upload-time = "2026-04-10T14:23:54.708Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/2c/9e/d7f18bd9a0354088abc11a0c1f2c7698f7c49e5a709faedf6a46e388f693/compressed_tensors-0.17.0.tar.gz", hash = "sha256:15c20d06bdbcf35b51fc99fd125e7b9be1e1855567c33b7a46dfac26ad6fb126", size = 257091, upload-time = "2026-06-03T16:49:17.208Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a8/52/93833dc1610e017ac5b7dcd59b8304d8ef67d1114c2d124e728a2cbbea12/compressed_tensors-0.15.0.1-py3-none-any.whl", hash = "sha256:e1b1f322e82e475715e242bad46925a304ea8e5c98b5055a15b8eb22fb6bfea9", size = 194260, upload-time = "2026-04-10T14:23:53.098Z" },
+    { url = "https://files.pythonhosted.org/packages/35/63/6edf0415b072fff0bf8b546074dea3f0f9b148e49b601ac98bdc60a76c68/compressed_tensors-0.17.0-py3-none-any.whl", hash = "sha256:4a1b89b508f7efb8ffb4eee8a6e69e0452d9b080cae130146025c64fbe9fa9aa", size = 211714, upload-time = "2026-06-03T16:49:15.672Z" },
 ]
 
 [[package]]
@@ -936,12 +936,54 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/cd/08/b5e3b9822662d72d540d830531e3ab6a7cabbda3dd56175696aabccfeb76/cuda_python-13.1.1-py3-none-any.whl", hash = "sha256:944cc4fe6482673d28dd545797a28840945a1668739328fa2ad1e9be4f7050d9", size = 8038, upload-time = "2025-12-09T22:13:10.719Z" },
 ]
 
+[[package]]
+name = "cuda-tile"
+version = "1.3.0"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+]
+dependencies = [
+    { name = "typing-extensions", marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-21-nemo-safe-synthesizer-cpu' and extra == 'extra-21-nemo-safe-synthesizer-cu129') or (platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-21-nemo-safe-synthesizer-cu129') or (platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-21-nemo-safe-synthesizer-cu129') or (sys_platform != 'linux' and extra == 'extra-21-nemo-safe-synthesizer-cpu' and extra == 'extra-21-nemo-safe-synthesizer-cu129')" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f4/d6/753aecb3e8fcee80d20f9d32b4504276691c2f77fc10abbbd8e82197e24c/cuda_tile-1.3.0-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:59d9843fa723ceb4d680ec246e12e3ded857266e4c2bf5c5d21e530d6d765060", size = 245441, upload-time = "2026-04-20T15:51:06.618Z" },
+    { url = "https://files.pythonhosted.org/packages/c5/2d/8b416239413bf11d17d42ccee43258f3787da13bcea7b2e42e8bbf04b3da/cuda_tile-1.3.0-cp311-cp311-manylinux2014_x86_64.whl", hash = "sha256:2888d6b89fae053a53ca7bb703c508a5cf90671d266934573c5b6c25978022c4", size = 246706, upload-time = "2026-04-20T15:51:03.467Z" },
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     { name = "tokenizers", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
+    { name = "tokenspeed-mla", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
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 [[package]]
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-version = "0.20.0+cu129"
-source = { registry = "https://wheels.vllm.ai/88d34c6409e9fb3c7b8ca0c04756f061d2099eb1/cu129" }
+version = "0.24.0+cu129"
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 resolution-markers = [
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@@ -6573,8 +6948,9 @@ dependencies = [
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     { name = "ijson", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
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@@ -6610,13 +6986,16 @@ dependencies = [
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     { name = "requests", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
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     { name = "tokenizers", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
+    { name = "tokenspeed-mla", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
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@@ -6627,8 +7006,8 @@ dependencies = [
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 [[package]]