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[CI/Build] Add markdown linter (#11857)

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Signed-off-by: Rafael Vasquez <rafvasq21@gmail.com>
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Rafael Vasquez
2025-01-12 03:17:13 -05:00
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parent b25cfab9a0
commit 43f3d9e699
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docs/source/features/quantization/auto_awq.md
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@@ -15,7 +15,7 @@ The main benefits are lower latency and memory usage.
You can quantize your own models by installing AutoAWQ or picking one of the [400+ models on Huggingface](https://huggingface.co/models?sort=trending&search=awq).
```console
$ pip install autoawq
pip install autoawq
```
After installing AutoAWQ, you are ready to quantize a model. Here is an example of how to quantize `mistralai/Mistral-7B-Instruct-v0.2`:
@@ -47,7 +47,7 @@ print(f'Model is quantized and saved at "{quant_path}"')
To run an AWQ model with vLLM, you can use [TheBloke/Llama-2-7b-Chat-AWQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-AWQ) with the following command:
```console
$ python examples/offline_inference/llm_engine_example.py --model TheBloke/Llama-2-7b-Chat-AWQ --quantization awq
python examples/offline_inference/llm_engine_example.py --model TheBloke/Llama-2-7b-Chat-AWQ --quantization awq
```
AWQ models are also supported directly through the LLM entrypoint: