[codex] Fix ModelScope remote GGUF quant loading

[glaziermag]

Fixes #41475.

Purpose

Fix ModelScope handling for remote GGUF selectors such as repo:Q4_K_M under VLLM_USE_MODELSCOPE=True.

The reported command crashed before model loading because speculator config probing passed a Path object into the ModelScope-patched PretrainedConfig.get_config_dict(...) path. A100 validation then found two additional same-command blockers after the initial Path.replace fix: vLLM selected the repo broadly enough to download every GGUF quant variant, and the selected Qwen3.5-MoE GGUF still failed during language-model-only startup.

Fix

• Keep remote GGUF repo ids as strings during speculator config probing.
• During ModelScope speculator probing, add *.gguf to ignore_file_pattern so metadata probing does not download GGUF weights.
• Preserve the selected GGUF quant suffix through config, tokenizer, and weight-download paths.
• Support dotted GGUF quant filenames such as model.Q4_K_M.gguf in selector matching.
• For ModelScope remote GGUF config loading, resolve and localize only the selected repo:quant file when the repo lacks config.json.
• Route download_gguf() through ModelScope when VLLM_USE_MODELSCOPE=True, preserving selected quant patterns.
• Infer the base HF model id from GGUF metadata when the selected GGUF architecture is not directly supported by Transformers config loading.
• Use the inferred base tokenizer for default-tokenizer --language-model-only remote GGUF runs.
• Treat multimodal-wrapper GGUFs as text-only under --language-model-only, skipping mm_proj while mapping text weights into the wrapped language model.
• Add Qwen3.5-MoE GGUF mapping/load support needed by the reported model, including tuple-shard handling and singleton output/conv reshape handling.

A100 validation update (2026-05-07)

Validated on GCP vllm-35922-a100, a2-highgpu-1g, 1x NVIDIA A100-SXM4-40GB, driver 580.126.20, CUDA reported by nvidia-smi as 13.0, Ubuntu 22.04.5, Python 3.10.12. Dependency versions: torch 2.11.0+cu130, transformers 5.8.0, modelscope 1.36.3, huggingface-hub 1.14.0, pytest 9.0.3, gguf import available. Initial validation was run from the PR checkout in a venv on the A100 host. Docker parity validation was also added by overlaying the PR Python files into the installed package.

Reporter-style command tested:

VLLM_USE_MODELSCOPE=True python -m vllm.entrypoints.cli.main serve \
  --model hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M \
  --max-model-len 102400 \
  --kv-cache-dtype fp8 \
  --gpu-memory-utilization 0.9 \
  --max-num-seqs 24 \
  --max-num-batched-tokens 8192 \
  --language-model-only \
  --enable-prefix-caching \
  --default-chat-template-kwargs '{"enable_thinking":false}'

Before/after:

• base 5737770c6c346d918fdfb13e9378f9514f616186: reproduced the original config-probing crash before model loading: TypeError: Path.replace() takes 2 positional arguments but 3 were given.
• original PR head a9713d224bc53de7b92ebb59a1a94c6caa88e310 (pre-signoff; same tree as signed-off 5e0f99307): no Path.replace crash, but the same command still selected/downloaded multiple GGUF variants (Q4_K_M, Q5_K_M, Q6_K, Q8_0) and did not validate full server startup.
• fixed head 1adea4bd315d319ce27f56bf84348dfcd369f71c (same tree as A100-validated pre-signoff 725312340813c2295626d028c863f941b0d97325): completed selected Q4_K_M download only, then the exact command reached server startup. Runtime log showed model loading at 19.87 GiB and 64.726065 seconds, profiling/warmup completion, GPU KV cache size: 1,366,337 tokens, and Application startup complete. The clean validation log had no missing-parameter, skip loading, traceback, KeyError, ValueError, AssertionError, or RuntimeError signatures.

Selected-download validation also completed the full 19.7 GB Q4 download and confirmed the cache contained Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled.Q4_K_M.gguf while Q5_K_M, Q6_K, and Q8_0 were absent.

Tests

On the A100 VM:

python -m ruff check \
  vllm/transformers_utils/config.py \
  vllm/transformers_utils/gguf_utils.py \
  vllm/config/model.py \
  vllm/model_executor/model_loader/gguf_loader.py \
  vllm/model_executor/models/qwen3_5.py \
  tests/transformers_utils/test_config.py \
  tests/models/test_gguf_download.py
python -m ruff format --check \
  vllm/transformers_utils/config.py \
  vllm/transformers_utils/gguf_utils.py \
  vllm/config/model.py \
  vllm/model_executor/model_loader/gguf_loader.py \
  vllm/model_executor/models/qwen3_5.py \
  tests/transformers_utils/test_config.py \
  tests/models/test_gguf_download.py
python -m pytest \
  tests/transformers_utils/test_config.py::test_ensure_transformers_can_check_gguf_version \
  tests/transformers_utils/test_config.py::test_get_gguf_base_model_id_for_config \
  tests/transformers_utils/test_config.py::test_get_config_falls_back_to_gguf_base_model \
  tests/transformers_utils/test_config.py::test_modelscope_remote_gguf_config_downloads_selected_file \
  tests/transformers_utils/test_config.py::test_maybe_override_with_speculators_gguf_quant_modelscope_no_path_replace_crash \
  tests/models/test_gguf_download.py::TestGGUFModelLoader::test_qwen35_moe_gguf_mapping \
  tests/models/test_gguf_download.py::TestGGUFModelLoader::test_qwen35_moe_gguf_language_model_only_mapping \
  tests/models/test_gguf_download.py::TestGGUFModelLoader::test_language_model_only_weight_types_skip_mmproj \
  tests/models/test_gguf_download.py::TestGGUFModelLoader::test_language_model_only_weights_iterator_skips_mmproj \
  tests/models/test_gguf_download.py::TestGGUFModelLoader::test_qwen35_gguf_tuple_shard_weight_splits \
  tests/models/test_gguf_download.py::TestGGUFModelLoader::test_qwen35_gguf_tuple_shard_weight_type_reuses_scalar \
  tests/models/test_gguf_download.py::TestGGUFModelLoader::test_qwen35_gguf_single_output_weight_unsqueeze \
  tests/models/test_gguf_download.py::TestGGUFModelLoader::test_qwen35_gguf_conv_weight_unsqueeze \
  -q --confcutdir=tests

Result: 13 passed.

Safe claim

This is validated as a full startup fix for the reported ModelScope remote GGUF repo:quant command on the A100 host. It does not claim generation-quality validation or exact reporter Docker image parity.

Docker parity validation update (2026-05-07)

Also checked the reporter-style Docker setup on the same A100 VM after installing Docker 29.1.3 and NVIDIA container runtime. Image used: vllm/vllm-openai:v0.20.0 at digest sha256:04563c302537a91aa49ebdfbceda96111c5712275999b7e8804fa598f0b5641d.

This was not a full rebuilt PR Docker image. To isolate container parity without a multi-hour image build, I mounted the PR checkout and overlaid the changed Python files into the installed vLLM package inside the reporter image before running vllm serve.

Docker before/after:

• unmodified vllm/vllm-openai:v0.20.0: reproduced the original Docker crash: TypeError: Path.replace() takes 2 positional arguments but 3 were given.
• patched overlay, standard --gpus all: passed the original ModelScope/GGUF path, downloaded only the selected Q4_K_M GGUF, resolved Qwen3_5MoeForCausalLM, then failed before model load with Docker GPU visibility in the vLLM EngineCore child: DP adjusted local rank 0 is out of bounds. A direct parent/child torch preflight in the same image saw CUDA, so this was treated as a container runtime/capability issue, not a PR path failure.
• patched overlay with explicit --runtime=nvidia --gpus all --privileged: reached server startup. Preflight printed CUDA visible as True 1 1; EngineCore initialized NCCL with world_size=1; model loading took 19.85 GiB and 72.970768 seconds; KV cache size was 364,480 tokens; max concurrency for 102,400-token requests was 13.35x; log reached Application startup complete.

The successful Docker log had no Path.replace, skip loading, missing-parameter, traceback, KeyError, ValueError, AssertionError, or RuntimeError signatures.

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[gemini-code-assist]

Code Review

This pull request fixes a crash in maybe_override_with_speculators occurring with remote GGUF models by ensuring the repository ID is handled as a string rather than a Path object. The changes also include comprehensive unit tests covering remote GGUF models, regular models, and local paths to prevent regressions. I have no feedback to provide.

[glaziermag]

Superseded. This was the first A100 validation note for head 725312340; the final validation for head 1adea4bd3 is here: #41488 (comment)

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[glaziermag]

Superseded. This was the intermediate A100 validation note for head 14a4b62d4; the final validation for head 1adea4bd3 is here: #41488 (comment)

[glaziermag]

A100 revalidation update after the latest push (1adea4bd3):

• Base 5737770c6c346d918fdfb13e9378f9514f616186 reproduces the reported ModelScope Path.replace() crash before model loading.
• Original PR head a9713d224bc53de7b92ebb59a1a94c6caa88e310 avoids that crash but still broad-downloads multiple GGUF quant variants and does not validate full startup.
• Fixed head 1adea4bd315d319ce27f56bf84348dfcd369f71c completed the selected Q4_K_M download only and the exact reporter-style command reached Application startup complete on the A100.

Environment: GCP vllm-35922-a100, A100-SXM4-40GB, driver 580.126.20, CUDA reported by nvidia-smi as 13.0, Ubuntu 22.04.5, Python 3.10.12, torch 2.11.0+cu130, transformers 5.8.0, modelscope 1.36.3.

Focused checks on the A100: ruff passed, format check passed, and the focused pytest set passed (13 passed). Full details are in the updated PR description.

[glaziermag]

DCO is now passing after signing all PR commits. The remaining pre-run-check failure is the repository gate requiring either ready/verified label or 4+ merged PRs by the author. I do not have permission to add ready; a maintainer will need to apply ready or verified to trigger the normal test path.

[glaziermag]

Duplicate Docker parity update; keeping the detailed version here: #41488 (comment)

[glaziermag]

Docker parity update on the A100 VM:

• Pulled reporter image vllm/vllm-openai:v0.20.0 (sha256:04563c302537a91aa49ebdfbceda96111c5712275999b7e8804fa598f0b5641d).
• Unmodified image reproduced the original Docker failure: TypeError: Path.replace() takes 2 positional arguments but 3 were given.
• Mounted the PR checkout and overlaid the changed Python files into the installed package inside the image.
• Patched overlay downloaded only Q4_K_M.gguf for the remote GGUF selector.
• With explicit --runtime=nvidia --gpus all --privileged, the patched overlay reached Application startup complete; model load was 19.85 GiB / 72.970768s, KV cache 364,480 tokens.

Caveat: this was an official-image Python overlay, not a full rebuilt PR Docker image. A non-privileged --gpus all overlay run got past the PR bug and selected download, but the vLLM EngineCore child saw zero CUDA devices (DP adjusted local rank 0 is out of bounds), while a direct torch parent/child preflight in the same image saw CUDA. I treated that as Docker runtime/capability behavior and confirmed startup with --privileged.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @glaziermag.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[glaziermag]

Wave 1 evidence bundle index:

PR: #41488
Linked issue: #41475
Base SHA: 5a0a8fc1ea7542394ff315138bd5677b7b53bca1 (PR base); validation base in the existing A100 note: 5737770c6c346d918fdfb13e9378f9514f616186
Current PR-head SHA: 1adea4bd3d336f200e9c64cee0a87f16f6ac918d
Fixed-head SHA, if changed: N/A

Exact command validated in the existing A100 note:

VLLM_USE_MODELSCOPE=True python -m vllm.entrypoints.cli.main serve \
  --model hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF:Q4_K_M \
  --max-model-len 102400 \
  --kv-cache-dtype fp8 \
  --gpu-memory-utilization 0.9 \
  --max-num-seqs 24 \
  --max-num-batched-tokens 8192 \
  --language-model-only \
  --enable-prefix-caching \
  --default-chat-template-kwargs '{"enable_thinking":false}'

Environment: GCP vllm-35922-a100, a2-highgpu-1g, 1x NVIDIA A100-SXM4-40GB, driver 580.126.20, nvidia-smi CUDA 13.0, Ubuntu 22.04.5, Python 3.10.12, torch 2.11.0+cu130, transformers 5.8.0, modelscope 1.36.3.
A100 category: A100_HOST_OPTIONAL for the reported config/startup path unless reviewers require actual GPU load as part of the claim; existing evidence did include A100 startup.

Base result: reproduced the reported ModelScope Path.replace() crash before model loading.
Current PR-head result: existing evidence comment for latest push prefix 1adea4bd3 reports selected Q4_K_M download only and Application startup complete; Docker overlay parity also reached startup with explicit NVIDIA runtime/privileged flags.
Tests added/changed: config/ModelScope/GGUF download/model mapping tests listed in the PR body.
Tests passed: ruff, format check, focused pytest set (13 passed) per existing A100 comment.
Side-effect controls: selected-download validation confirmed no broad Q5_K_M, Q6_K, or Q8_0 downloads; generation quality is not claimed.
Raw logs/artifacts: existing comments #41488 (comment) and #41488 (comment) plus PR-body validation section. No separate raw log file is attached in this comment.
Remaining risks: the Docker parity path used a Python overlay rather than a full rebuilt PR Docker image; generation quality was not tested.
Can say “Fixes #issue”: yes for #41475 startup for the reported ModelScope remote GGUF repo:quant command.
Safe wording: “Fixes #41475 startup for the reported ModelScope remote GGUF repo:quant command; generation quality is not claimed.”
Readiness status: ready-now if existing A100 comments/PR-body excerpts are accepted as artifacts; otherwise standalone raw logs remain to attach.

[glaziermag]

Ready for maintainer review. Evidence attached shows the original/base failure and current-head pass under the scoped issue conditions. The PR may use the Fixes #... wording already present in the body.