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

Signed-off-by: windsonsea <[email protected]>

Author: windsonsea

docs/source/features/quantization/gptqmodel.md

@@ -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)
 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).
 
 ```console
 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.
 
 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)
 ```
 
+## 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:
 
 ```console
 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:
 
 ```python
@@ -67,14 +75,17 @@ prompts = [
     "The capital of France is",
     "The future of AI is",
 ]
+
 # Create a sampling params object.
 sampling_params = SamplingParams(temperature=0.6, top_p=0.9)
 
 # Create an LLM.
 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
 # that contain the prompt, generated text, and other information.
 outputs = llm.generate(prompts, sampling_params)
+
 # Print the outputs.
 for output in outputs:
     prompt = output.prompt