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

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

docs/features/compatibility_matrix.md

@@ -36,9 +36,9 @@ th:not(:first-child) {
 
 | Feature | [CP][chunked-prefill] | [APC](automatic_prefix_caching.md) | [LoRA](lora.md) | [SD](spec_decode.md) | CUDA graph | [pooling](../models/pooling_models.md) | <abbr title="Encoder-Decoder Models">enc-dec</abbr> | <abbr title="Logprobs">logP</abbr> | <abbr title="Prompt Logprobs">prmpt logP</abbr> | <abbr title="Async Output Processing">async output</abbr> | multi-step | <abbr title="Multimodal Inputs">mm</abbr> | best-of | beam-search |
 |---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
-| [CP][chunked-prefill] | ✅ | | | | | | | | | | | | | | |
-| [APC](automatic_prefix_caching.md) | ✅ | ✅ | | | | | | | | | | | | | |
-| [LoRA](lora.md) | ✅ | ✅ | ✅ | | | | | | | | | | | | |
+| [CP][chunked-prefill] | ✅ | | | | | | | | | | | | | |
+| [APC](automatic_prefix_caching.md) | ✅ | ✅ | | | | | | | | | | | | |
+| [LoRA](lora.md) | ✅ | ✅ | ✅ | | | | | | | | | | | |
 | [SD](spec_decode.md) | ✅ | ✅ | ❌ | ✅ | | | | | | | | | | |
 | CUDA graph | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | |
 | [pooling](../models/pooling_models.md) | ✅\* | ✅\* | ✅ | ❌ | ✅ | ✅ | | | | | | | | |

docs/features/lora.md

@@ -119,6 +119,7 @@ export VLLM_ALLOW_RUNTIME_LORA_UPDATING=True
 ```
 
 ### Using API Endpoints
+
 Loading a LoRA Adapter:
 
 To dynamically load a LoRA adapter, send a POST request to the `/v1/load_lora_adapter` endpoint with the necessary
@@ -156,6 +157,7 @@ curl -X POST http://localhost:8000/v1/unload_lora_adapter \
 ```
 
 ### Using Plugins
+
 Alternatively, you can use the LoRAResolver plugin to dynamically load LoRA adapters. LoRAResolver plugins enable you to load LoRA adapters from both local and remote sources such as local file system and S3. On every request, when there's a new model name that hasn't been loaded yet, the LoRAResolver will try to resolve and load the corresponding LoRA adapter.
 
 You can set up multiple LoRAResolver plugins if you want to load LoRA adapters from different sources. For example, you might have one resolver for local files and another for S3 storage. vLLM will load the first LoRA adapter that it finds.

docs/features/multimodal_inputs.md

@@ -588,7 +588,9 @@ Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for
 
 To input pre-computed embeddings belonging to a data type (i.e. image, video, or audio) directly to the language model,
 pass a tensor of shape to the corresponding field of the multi-modal dictionary.
+
 #### Image Embedding Inputs
+
 For image embeddings, you can pass the base64-encoded tensor to the `image_embeds` field.
 The following example demonstrates how to pass image embeddings to the OpenAI server:
 

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