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[Install] Include GGUF and STT dependencies by default - #1161
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WindChimeRan merged 2 commits intoOct 20, 2026
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Signed-off-by: Ranran <ranranhaoranzhang@gmail.com>
WindChimeRan
marked this pull request as draft
October 19, 2026 20:38
Signed-off-by: Ranran <ranranhaoranzhang@gmail.com>
WindChimeRan
marked this pull request as ready for review
October 19, 2026 21:40
LxYuan0420
approved these changes
Oct 20, 2026
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Motivation
Homebrew installs the base wheel without the GGUF/STT extras, while the feature docs require additional pip commands. Make
gguf,librosa, andnumbarequired dependencies so the standard installation includes both features. Remove the obsolete extras, installation sections, and missing-extra handling.Note that this is intentionally different from upstream vllm (who has audio, extra-quant options), because I think vllm is closer to power users who mainly use uv, while vllm-metal can have more non-technical users, who mainly use homebrew.
Cost
The cost is small: dependency resolution adds about 0.12 MB for GGUF and 10.9 MB for STT in compressed wheels on macOS arm64/Python 3.12.
NumPy, SciPy, Numba, and llvmlite are already in the base dependency set. Homebrew picks this up with the next plugin release and formula update.
Validation
On macOS 27.0.1 arm64 / Python 3.12:
./install.shand a freshpip install <vLLM wheel> <built plugin wheel>without extras, following Homebrew's two-wheel installation pattern. Dependency checks, GGUF imports, audio decoding, and a prebuilt Metal kernel passed. The Homebrew command itself was not run.uv build --wheel --python .venv-vllm-metal/bin/pythonandverify_wheel_artifacts: passed; required dependency metadata verified; only[dev]remains.scripts/lint.sh: passed.pytest -m 'not slow' tests/test_gguf_vllm_integration.py tests/test_model_lifecycle.py tests/test_stt.py tests/test_stt_sampling.py tests/test_stt_serve.py tests/test_stt_smoke_tool.py tests/test_v1_stt_integration.py tests/test_whisper.py tests/test_qwen3_asr.py -q: 301 passed, 5 deselected.tools/stt_sampling_serve_smoke.py --model <cached whisper-tiny snapshot> --audio speech.wav, from the clean wheel environment: 9 HTTP 200 responses, all 6 checks passed (greedy, sampling, seeded repeatability, top-k, silence, and language detection).