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

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

docs/deployment/frameworks/haystack.md

 # Haystack
 
-# Haystack
-
 [Haystack](https://github.com/deepset-ai/haystack) is an end-to-end LLM framework that allows you to build applications powered by LLMs, Transformer models, vector search and more. Whether you want to perform retrieval-augmented generation (RAG), document search, question answering or answer generation, Haystack can orchestrate state-of-the-art embedding models and LLMs into pipelines to build end-to-end NLP applications and solve your use case.
 
 It allows you to deploy a large language model (LLM) server with vLLM as the backend, which exposes OpenAI-compatible endpoints.

docs/deployment/frameworks/retrieval_augmented_generation.md

@@ -3,6 +3,7 @@
 [Retrieval-augmented generation (RAG)](https://en.wikipedia.org/wiki/Retrieval-augmented_generation) is a technique that enables generative artificial intelligence (Gen AI) models to retrieve and incorporate new information. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to supplement information from its pre-existing training data. This allows LLMs to use domain-specific and/or updated information. Use cases include providing chatbot access to internal company data or generating responses based on authoritative sources.
 
 Here are the integrations:
+
 - vLLM + [langchain](https://github.com/langchain-ai/langchain) + [milvus](https://github.com/milvus-io/milvus)
 - vLLM + [llamaindex](https://github.com/run-llama/llama_index) + [milvus](https://github.com/milvus-io/milvus)
 

docs/deployment/integrations/production-stack.md

@@ -140,6 +140,7 @@ The core vLLM production stack configuration is managed with YAML. Here is the e
     ```
 
 In this YAML configuration:
+
 * **`modelSpec`** includes:
     * `name`: A nickname that you prefer to call the model.
     * `repository`: Docker repository of vLLM.

docs/design/metrics.md

@@ -361,7 +361,7 @@ instances in Prometheus.
 
 We use this concept for the `vllm:cache_config_info` metric:
 
-```
+```text
 # HELP vllm:cache_config_info Information of the LLMEngine CacheConfig
 # TYPE vllm:cache_config_info gauge
 vllm:cache_config_info{block_size="16",cache_dtype="auto",calculate_kv_scales="False",cpu_offload_gb="0",enable_prefix_caching="False",gpu_memory_utilization="0.9",...} 1.0
@@ -686,7 +686,7 @@ documentation for this option states:
 The metrics were added by <gh-pr:7089> and who up in an OpenTelemetry trace
 as:
 
-```
+```text
 -> gen_ai.latency.time_in_scheduler: Double(0.017550230026245117)
 -> gen_ai.latency.time_in_model_forward: Double(3.151565277099609)
 -> gen_ai.latency.time_in_model_execute: Double(3.6468167304992676)

docs/design/p2p_nccl_connector.md

@@ -5,6 +5,7 @@ An implementation of xPyD with dynamic scaling based on point-to-point communica
 ## Detailed Design
 
 ### Overall Process
+
 As shown in Figure 1, the overall process of this **PD disaggregation** solution is described through a request flow:
 
 1. The client sends an HTTP request to the Proxy/Router's `/v1/completions` interface.
@@ -23,7 +24,7 @@ A simple HTTP service acts as the entry point for client requests and starts a b
 
 The Proxy/Router is responsible for selecting 1P1D based on the characteristics of the client request, such as the prompt, and generating a corresponding `request_id`, for example:
 
-```
+```text
 cmpl-___prefill_addr_10.0.1.2:21001___decode_addr_10.0.1.3:22001_93923d63113b4b338973f24d19d4bf11-0
 ```
 
@@ -70,6 +71,7 @@ pip install "vllm>=0.9.2"
 ## Run xPyD
 
 ### Instructions
+
 - The following examples are run on an A800 (80GB) device, using the Meta-Llama-3.1-8B-Instruct model.
 - Pay attention to the setting of the `kv_buffer_size` (in bytes). The empirical value is 10% of the GPU memory size. This is related to the kvcache size. If it is too small, the GPU memory buffer for temporarily storing the received kvcache will overflow, causing the kvcache to be stored in the tensor memory pool, which increases latency. If it is too large, the kvcache available for inference will be reduced, leading to a smaller batch size and decreased throughput.
 - For Prefill instances, when using non-GET mode, the `kv_buffer_size` can be set to 1, as Prefill currently does not need to receive kvcache. However, when using GET mode, a larger `kv_buffer_size` is required because it needs to store the kvcache sent to the D instance.

docs/design/prefix_caching.md

@@ -18,10 +18,12 @@ In the example above, the KV cache in the first block can be uniquely identified
 * Block tokens: A tuple of tokens in this block. The reason to include the exact tokens is to reduce potential hash value collision.
 * Extra hashes: Other values required to make this block unique, such as LoRA IDs, multi-modality input hashes (see the example below), and cache salts to isolate caches in multi-tenant environments.
 
-> **Note 1:** We only cache full blocks.
+!!! note "Note 1"
+    We only cache full blocks.
 
-> **Note 2:** The above hash key structure is not 100% collision free. Theoretically it’s still possible for the different prefix tokens to have the same hash value. To avoid any hash collisions **in a multi-tenant setup, we advise to use SHA256** as hash function instead of the default builtin hash.
-SHA256 is supported since vLLM v0.8.3 and must be enabled with a command line argument. It comes with a performance impact of about 100-200ns per token (~6ms for 50k tokens of context).
+!!! note "Note 2"
+    The above hash key structure is not 100% collision free. Theoretically it’s still possible for the different prefix tokens to have the same hash value. To avoid any hash collisions **in a multi-tenant setup, we advise to use SHA256** as hash function instead of the default builtin hash.
+    SHA256 is supported since vLLM v0.8.3 and must be enabled with a command line argument. It comes with a performance impact of about 100-200ns per token (~6ms for 50k tokens of context).
 
 **A hashing example with multi-modality inputs**  
 In this example, we illustrate how prefix caching works with multi-modality inputs (e.g., images). Assuming we have a request with the following messages:
@@ -92,7 +94,8 @@ To improve privacy in shared environments, vLLM supports isolating prefix cache
 
 With this setup, cache sharing is limited to users or requests that explicitly agree on a common salt, enabling cache reuse within a trust group while isolating others.
 
-> **Note:** Cache isolation is not supported in engine V0.
+!!! note
+    Cache isolation is not supported in engine V0.
 
 ## Data Structure
 

docs/design/torch_compile.md

@@ -8,7 +8,7 @@ Throughout the example, we will run a common Llama model using v1, and turn on d
 
 In the very verbose logs, we can see:
 
-```
+```console
 INFO 03-07 03:06:55 [backends.py:409] Using cache directory: ~/.cache/vllm/torch_compile_cache/1517964802/rank_0_0 for vLLM's torch.compile
 ```
 
@@ -75,7 +75,7 @@ Every submodule can be identified by its index, and will be processed individual
 
 In the very verbose logs, we can also see:
 
-```
+```console
 DEBUG 03-07 03:52:37 [backends.py:134] store the 0-th graph for shape None from inductor via handle ('fpegyiq3v3wzjzphd45wkflpabggdbjpylgr7tta4hj6uplstsiw', '~/.cache/vllm/torch_compile_cache/1517964802/rank_0_0/inductor_cache/iw/ciwzrk3ittdqatuzwonnajywvno3llvjcs2vfdldzwzozn3zi3iy.py')
 DEBUG 03-07 03:52:39 [backends.py:134] store the 1-th graph for shape None from inductor via handle ('f7fmlodmf3h3by5iiu2c4zarwoxbg4eytwr3ujdd2jphl4pospfd', '~/.cache/vllm/torch_compile_cache/1517964802/rank_0_0/inductor_cache/ly/clyfzxldfsj7ehaluis2mca2omqka4r7mgcedlf6xfjh645nw6k2.py')
 ...
@@ -93,7 +93,7 @@ One more detail: you can see that the 1-th graph and the 15-th graph have the sa
 
 If we already have the cache directory (e.g. run the same code for the second time), we will see the following logs:
 
-```
+```console
 DEBUG 03-07 04:00:45 [backends.py:86] Directly load the 0-th graph for shape None from inductor via handle ('fpegyiq3v3wzjzphd45wkflpabggdbjpylgr7tta4hj6uplstsiw', '~/.cache/vllm/torch_compile_cache/1517964802/rank_0_0/inductor_cache/iw/ciwzrk3ittdqatuzwonnajywvno3llvjcs2vfdldzwzozn3zi3iy.py')
 ```
 

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/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

docs/getting_started/installation/cpu.md

@@ -164,7 +164,7 @@ Note, it is recommended to manually reserve 1 CPU for vLLM front-end process whe
 
 ### How to decide `VLLM_CPU_KVCACHE_SPACE`?
 
-  - This value is 4GB by default. Larger space can support more concurrent requests, longer context length. However, users should take care of memory capacity of each NUMA node. The memory usage of each TP rank is the sum of `weight shard size` and `VLLM_CPU_KVCACHE_SPACE`, if it exceeds the capacity of a single NUMA node, the TP worker will be killed with `exitcode 9` due to out-of-memory.
+This value is 4GB by default. Larger space can support more concurrent requests, longer context length. However, users should take care of memory capacity of each NUMA node. The memory usage of each TP rank is the sum of `weight shard size` and `VLLM_CPU_KVCACHE_SPACE`, if it exceeds the capacity of a single NUMA node, the TP worker will be killed with `exitcode 9` due to out-of-memory.
 
 ### How to do performance tuning for vLLM CPU?
 
@@ -183,13 +183,13 @@ vLLM CPU supports tensor parallel (TP) and pipeline parallel (PP) to leverage mu
 
 ### Which quantization configs does vLLM CPU support?
 
-  - vLLM CPU supports quantizations:
+- vLLM CPU supports quantizations:
     - AWQ (x86 only)
     - GPTQ (x86 only)
     - compressed-tensor INT8 W8A8 (x86, s390x)
 
 ### (x86 only) What is the purpose of `VLLM_CPU_MOE_PREPACK` and `VLLM_CPU_SGL_KERNEL`?
 
-  - Both of them requires `amx` CPU flag.
+- Both of them requires `amx` CPU flag.
     - `VLLM_CPU_MOE_PREPACK` can provides better performance for MoE models
     - `VLLM_CPU_SGL_KERNEL` can provides better performance for MoE models and small-batch scenarios.