[Docs] Switch to better markdown linting pre-commit hook (#21851)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

docs/features/quantization/auto_round.md

@@ -97,7 +97,7 @@ for output in outputs:
     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
 ```
 
-# Acknowledgement
+## Acknowledgement
 
 Special thanks to open-source low precision libraries such as AutoGPTQ, AutoAWQ, GPTQModel, Triton, Marlin, and
 ExLLaMAV2 for providing low-precision CUDA kernels, which are leveraged in AutoRound.

docs/features/quantization/int4.md

@@ -134,8 +134,8 @@ lm_eval --model vllm \
 - Employ the chat template or instruction template that the model was trained with
 - If you've fine-tuned a model, consider using a sample of your training data for calibration
 - Tune key hyperparameters to the quantization algorithm:
-  - `dampening_frac` sets how much influence the GPTQ algorithm has. Lower values can improve accuracy, but can lead to numerical instabilities that cause the algorithm to fail.
-  - `actorder` sets the activation ordering. When compressing the weights of a layer weight, the order in which channels are quantized matters. Setting `actorder="weight"` can improve accuracy without added latency.
+    - `dampening_frac` sets how much influence the GPTQ algorithm has. Lower values can improve accuracy, but can lead to numerical instabilities that cause the algorithm to fail.
+    - `actorder` sets the activation ordering. When compressing the weights of a layer weight, the order in which channels are quantized matters. Setting `actorder="weight"` can improve accuracy without added latency.
 
 The following is an example of an expanded quantization recipe you can tune to your own use case:
 

docs/features/quantization/quantized_kvcache.md

@@ -50,6 +50,7 @@ Here is an example of how to enable FP8 quantization:
     ```
 
 The `kv_cache_dtype` argument specifies the data type for KV cache storage:
+
 - `"auto"`: Uses the model's default "unquantized" data type
 - `"fp8"` or `"fp8_e4m3"`: Supported on CUDA 11.8+ and ROCm (AMD GPU)
 - `"fp8_e5m2"`: Supported on CUDA 11.8+

docs/features/quantization/quark.md

@@ -213,6 +213,7 @@ lm_eval --model vllm \
 ```
 
 ## Quark Quantization Script
+
 In addition to the example of Python API above, Quark also offers a
 [quantization script](https://quark.docs.amd.com/latest/pytorch/example_quark_torch_llm_ptq.html)
 to quantize large language models more conveniently. It supports quantizing models with variety

docs/features/quantization/torchao.md

@@ -13,6 +13,7 @@ pip install \
 ```
 
 ## Quantizing HuggingFace Models
+
 You can quantize your own huggingface model with torchao, e.g. [transformers](https://huggingface.co/docs/transformers/main/en/quantization/torchao) and [diffusers](https://huggingface.co/docs/diffusers/en/quantization/torchao), and save the checkpoint to huggingface hub like [this](https://huggingface.co/jerryzh168/llama3-8b-int8wo) with the following example code:
 
 ??? code