Remove unnecessary explicit title anchors and use relative links instead (#20620)

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

docs/deployment/nginx.md

 ---
 title: Using Nginx
 ---
-[](){ #nginxloadbalancer }
 
 This document shows how to launch multiple vLLM serving containers and use Nginx to act as a load balancer between the servers.
 

docs/design/arch_overview.md

 ---
 title: Architecture Overview
 ---
-[](){ #arch-overview }
 
 This document provides an overview of the vLLM architecture.
 
@@ -74,7 +73,7 @@ python -m vllm.entrypoints.openai.api_server --model <model>
 
 That code can be found in <gh-file:vllm/entrypoints/openai/api_server.py>.
 
-More details on the API server can be found in the [OpenAI-Compatible Server][serving-openai-compatible-server] document.
+More details on the API server can be found in the [OpenAI-Compatible Server](../serving/openai_compatible_server.md) document.
 
 ## LLM Engine
 
@@ -132,7 +131,7 @@ input tensors and capturing cudagraphs.
 ## Model
 
 Every model runner object has one model object, which is the actual
-`torch.nn.Module` instance. See [huggingface_integration][huggingface-integration] for how various
+`torch.nn.Module` instance. See [huggingface_integration](huggingface_integration.md) for how various
 configurations affect the class we ultimately get.
 
 ## Class Hierarchy

docs/design/automatic_prefix_caching.md

 ---
 title: Automatic Prefix Caching
 ---
-[](){ #design-automatic-prefix-caching }
 
 The core idea of [PagedAttention](https://blog.vllm.ai/2023/06/20/vllm.html) is to partition the KV cache of each request into KV Blocks. Each block contains the attention keys and values for a fixed number of tokens. The PagedAttention algorithm allows these blocks to be stored in non-contiguous physical memory so that we can eliminate memory fragmentation by allocating the memory on demand.
 

docs/design/huggingface_integration.md

 ---
 title: Integration with HuggingFace
 ---
-[](){ #huggingface-integration }
 
 This document describes how vLLM integrates with HuggingFace libraries. We will explain step by step what happens under the hood when we run `vllm serve`.
 

docs/design/kernel/paged_attention.md

 ---
 title: vLLM Paged Attention
 ---
-[](){ #design-paged-attention }
 
 Currently, vLLM utilizes its own implementation of a multi-head query
 attention kernel (`csrc/attention/attention_kernels.cu`).

docs/design/mm_processing.md

 ---
 title: Multi-Modal Data Processing
 ---
-[](){ #mm-processing }
 
-To enable various optimizations in vLLM such as [chunked prefill][chunked-prefill] and [prefix caching][automatic-prefix-caching], we use [BaseMultiModalProcessor][vllm.multimodal.processing.BaseMultiModalProcessor] to provide the correspondence between placeholder feature tokens (e.g. `<image>`) and multi-modal inputs (e.g. the raw input image) based on the outputs of HF processor.
+To enable various optimizations in vLLM such as [chunked prefill][chunked-prefill] and [prefix caching](../features/automatic_prefix_caching.md), we use [BaseMultiModalProcessor][vllm.multimodal.processing.BaseMultiModalProcessor] to provide the correspondence between placeholder feature tokens (e.g. `<image>`) and multi-modal inputs (e.g. the raw input image) based on the outputs of HF processor.
 
 Here are the main features of [BaseMultiModalProcessor][vllm.multimodal.processing.BaseMultiModalProcessor]:
 

docs/design/plugin_system.md

 ---
 title: vLLM's Plugin System
 ---
-[](){ #plugin-system }
 
 The community frequently requests the ability to extend vLLM with custom features. To facilitate this, vLLM includes a plugin system that allows users to add custom features without modifying the vLLM codebase. This document explains how plugins work in vLLM and how to create a plugin for vLLM.
 
 ## How Plugins Work in vLLM
 
-Plugins are user-registered code that vLLM executes. Given vLLM's architecture (see [Arch Overview][arch-overview]), multiple processes may be involved, especially when using distributed inference with various parallelism techniques. To enable plugins successfully, every process created by vLLM needs to load the plugin. This is done by the [load_general_plugins](https://github.com/vllm-project/vllm/blob/c76ac49d266e27aa3fea84ef2df1f813d24c91c7/vllm/plugins/__init__.py#L16) function in the `vllm.plugins` module. This function is called for every process created by vLLM before it starts any work.
+Plugins are user-registered code that vLLM executes. Given vLLM's architecture (see [Arch Overview](arch_overview.md)), multiple processes may be involved, especially when using distributed inference with various parallelism techniques. To enable plugins successfully, every process created by vLLM needs to load the plugin. This is done by the [load_general_plugins](https://github.com/vllm-project/vllm/blob/c76ac49d266e27aa3fea84ef2df1f813d24c91c7/vllm/plugins/__init__.py#L16) function in the `vllm.plugins` module. This function is called for every process created by vLLM before it starts any work.
 
 ## How vLLM Discovers Plugins
 

docs/features/automatic_prefix_caching.md

 ---
 title: Automatic Prefix Caching
 ---
-[](){ #automatic-prefix-caching }
 
 ## Introduction
 
 Automatic Prefix Caching (APC in short) caches the KV cache of existing queries, so that a new query can directly reuse the KV cache if it shares the same prefix with one of the existing queries, allowing the new query to skip the computation of the shared part.
 
 !!! note
-    Technical details on how vLLM implements APC can be found [here][design-automatic-prefix-caching].
+    Technical details on how vLLM implements APC can be found [here](../design/automatic_prefix_caching.md).
 
 ## Enabling APC in vLLM
 

docs/features/compatibility_matrix.md

 ---
 title: Compatibility Matrix
 ---
-[](){ #compatibility-matrix }
 
 The tables below show mutually exclusive features and the support on some hardware.
 
@@ -37,13 +36,13 @@ th:not(:first-child) {
 }
 </style>
 
-| Feature | [CP][chunked-prefill] | [APC][automatic-prefix-caching] | [LoRA][lora-adapter] | <abbr title="Prompt Adapter">prmpt adptr</abbr> | [SD][spec-decode] | CUDA graph | <abbr title="Pooling Models">pooling</abbr> | <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 |
+| Feature | [CP][chunked-prefill] | [APC](automatic_prefix_caching.md) | [LoRA](lora.md) | <abbr title="Prompt Adapter">prmpt adptr</abbr> | [SD](spec_decode.md) | CUDA graph | <abbr title="Pooling Models">pooling</abbr> | <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] | ✅ | ✅ | | | | | | | | | | | | | |
-| [LoRA][lora-adapter] | ✅ | ✅ | ✅ | | | | | | | | | | | | |
+| [APC](automatic_prefix_caching.md) | ✅ | ✅ | | | | | | | | | | | | | |
+| [LoRA](lora.md) | ✅ | ✅ | ✅ | | | | | | | | | | | | |
 | <abbr title="Prompt Adapter">prmpt adptr</abbr> | ✅ | ✅ | ✅ | ✅ | | | | | | | | | | | |
-| [SD][spec-decode] | ✅ | ✅ | ❌ | ✅ | ✅ | | | | | | | | | | |
+| [SD](spec_decode.md) | ✅ | ✅ | ❌ | ✅ | ✅ | | | | | | | | | | |
 | CUDA graph | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | |
 | <abbr title="Pooling Models">pooling</abbr> | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | | | | | | | | |
 | <abbr title="Encoder-Decoder Models">enc-dec</abbr> | ❌ | [❌](gh-issue:7366) | ❌ | ❌ | [❌](gh-issue:7366) | ✅ | ✅ | ✅ | | | | | | | |
@@ -62,10 +61,10 @@ th:not(:first-child) {
 | Feature                                                   | Volta               | Turing    | Ampere    | Ada    | Hopper     | CPU                | AMD    | TPU |
 |-----------------------------------------------------------|---------------------|-----------|-----------|--------|------------|--------------------|--------|-----|
 | [CP][chunked-prefill]                                     | [❌](gh-issue:2729) | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ |
-| [APC][automatic-prefix-caching]                           | [❌](gh-issue:3687) | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ |
-| [LoRA][lora-adapter]                                      | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ |
+| [APC](automatic_prefix_caching.md)                           | [❌](gh-issue:3687) | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ |
+| [LoRA](lora.md)                                      | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ |
 | <abbr title="Prompt Adapter">prmpt adptr</abbr>           | ✅                  | ✅        | ✅        | ✅     | ✅        | [❌](gh-issue:8475) | ✅     | ❌ |
-| [SD][spec-decode]                                         | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ |
+| [SD](spec_decode.md)                                         | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ |
 | CUDA graph                                                | ✅                  | ✅        | ✅        | ✅     | ✅        | ❌                  | ✅     | ❌ |
 | <abbr title="Pooling Models">pooling</abbr>               | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ❔     | ❌ |
 | <abbr title="Encoder-Decoder Models">enc-dec</abbr>       | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ❌     | ❌ |

docs/features/disagg_prefill.md

 ---
 title: Disaggregated Prefilling (experimental)
 ---
-[](){ #disagg-prefill }
 
 This page introduces you the disaggregated prefilling feature in vLLM.
 

docs/features/lora.md

 ---
 title: LoRA Adapters
 ---
-[](){ #lora-adapter }
 
 This document shows you how to use [LoRA adapters](https://arxiv.org/abs/2106.09685) with vLLM on top of a base model.
 

docs/features/multimodal_inputs.md

 ---
 title: Multimodal Inputs
 ---
-[](){ #multimodal-inputs }
 
 This page teaches you how to pass multi-modal inputs to [multi-modal models][supported-mm-models] in vLLM.
 

docs/features/quantization/README.md

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 title: Quantization
 ---
-[](){ #quantization-index }
 
 Quantization trades off model precision for smaller memory footprint, allowing large models to be run on a wider range of devices.
 

docs/features/quantization/auto_awq.md

 ---
 title: AutoAWQ
 ---
-[](){ #auto-awq }
 
 To create a new 4-bit quantized model, you can leverage [AutoAWQ](https://github.com/casper-hansen/AutoAWQ).
 Quantization reduces the model's precision from BF16/FP16 to INT4 which effectively reduces the total model memory footprint.

docs/features/quantization/bitblas.md

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 title: BitBLAS
 ---
-[](){ #bitblas }
 
 vLLM now supports [BitBLAS](https://github.com/microsoft/BitBLAS) for more efficient and flexible model inference. Compared to other quantization frameworks, BitBLAS provides more precision combinations.
 

docs/features/quantization/bnb.md

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 title: BitsAndBytes
 ---
-[](){ #bits-and-bytes }
 
 vLLM now supports [BitsAndBytes](https://github.com/TimDettmers/bitsandbytes) for more efficient model inference.
 BitsAndBytes quantizes models to reduce memory usage and enhance performance without significantly sacrificing accuracy.

docs/features/quantization/fp8.md

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 title: FP8 W8A8
 ---
-[](){ #fp8 }
 
 vLLM supports FP8 (8-bit floating point) weight and activation quantization using hardware acceleration on GPUs such as Nvidia H100 and AMD MI300x.
 Currently, only Hopper and Ada Lovelace GPUs are officially supported for W8A8.

docs/features/quantization/gguf.md

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 title: GGUF
 ---
-[](){ #gguf }
 
 !!! warning
     Please note that GGUF support in vLLM is highly experimental and under-optimized at the moment, it might be incompatible with other features. Currently, you can use GGUF as a way to reduce memory footprint. If you encounter any issues, please report them to the vLLM team.

docs/features/quantization/gptqmodel.md

 ---
 title: GPTQModel
 ---
-[](){ #gptqmodel }
 
 To create a new 4-bit or 8-bit GPTQ quantized model, you can leverage [GPTQModel](https://github.com/ModelCloud/GPTQModel) from ModelCloud.AI.
 

docs/features/quantization/int4.md

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 title: INT4 W4A16
 ---
-[](){ #int4 }
 
 vLLM supports quantizing weights to INT4 for memory savings and inference acceleration. This quantization method is particularly useful for reducing model size and maintaining low latency in workloads with low queries per second (QPS).