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[Doc] Add headings to improve gptqmodel.md (#17164)

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@ -16,12 +16,16 @@ GPTQModel is one of the few quantization toolkits in the world that allows `Dyna
is fully integrated into vLLM and backed up by support from the ModelCloud.AI team. Please refer to [GPTQModel readme](https://github.com/ModelCloud/GPTQModel?tab=readme-ov-file#dynamic-quantization-per-module-quantizeconfig-override) is fully integrated into vLLM and backed up by support from the ModelCloud.AI team. Please refer to [GPTQModel readme](https://github.com/ModelCloud/GPTQModel?tab=readme-ov-file#dynamic-quantization-per-module-quantizeconfig-override)
for more details on this and other advanced features. for more details on this and other advanced features.
## Installation
You can quantize your own models by installing [GPTQModel](https://github.com/ModelCloud/GPTQModel) or picking one of the [5000+ models on Huggingface](https://huggingface.co/models?sort=trending&search=gptq). You can quantize your own models by installing [GPTQModel](https://github.com/ModelCloud/GPTQModel) or picking one of the [5000+ models on Huggingface](https://huggingface.co/models?sort=trending&search=gptq).
```console ```console
pip install -U gptqmodel --no-build-isolation -v pip install -U gptqmodel --no-build-isolation -v
``` ```
## Quantizing a model
After installing GPTQModel, you are ready to quantize a model. Please refer to the [GPTQModel readme](https://github.com/ModelCloud/GPTQModel/?tab=readme-ov-file#quantization) for further details. After installing GPTQModel, you are ready to quantize a model. Please refer to the [GPTQModel readme](https://github.com/ModelCloud/GPTQModel/?tab=readme-ov-file#quantization) for further details.
Here is an example of how to quantize `meta-llama/Llama-3.2-1B-Instruct`: Here is an example of how to quantize `meta-llama/Llama-3.2-1B-Instruct`:
@ -49,12 +53,16 @@ model.quantize(calibration_dataset, batch_size=2)
model.save(quant_path) model.save(quant_path)
``` ```
## Running a quantized model with vLLM
To run an GPTQModel quantized model with vLLM, you can use [DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2](https://huggingface.co/ModelCloud/DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2) with the following command: To run an GPTQModel quantized model with vLLM, you can use [DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2](https://huggingface.co/ModelCloud/DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2) with the following command:
```console ```console
python examples/offline_inference/llm_engine_example.py --model DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2 python examples/offline_inference/llm_engine_example.py --model DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2
``` ```
## Using GPTQModel with vLLM's Python API
GPTQModel quantized models are also supported directly through the LLM entrypoint: GPTQModel quantized models are also supported directly through the LLM entrypoint:
```python ```python
@ -67,14 +75,17 @@ prompts = [
"The capital of France is", "The capital of France is",
"The future of AI is", "The future of AI is",
] ]
# Create a sampling params object. # Create a sampling params object.
sampling_params = SamplingParams(temperature=0.6, top_p=0.9) sampling_params = SamplingParams(temperature=0.6, top_p=0.9)
# Create an LLM. # Create an LLM.
llm = LLM(model="DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2") llm = LLM(model="DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2")
# Generate texts from the prompts. The output is a list of RequestOutput objects # Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information. # that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params) outputs = llm.generate(prompts, sampling_params)
# Print the outputs. # Print the outputs.
for output in outputs: for output in outputs:
prompt = output.prompt prompt = output.prompt