Stop using title frontmatter and fix doc that can only be reached by search (#20623)

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

docs/design/arch_overview.md

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-title: Architecture Overview
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+# Architecture Overview
 
 This document provides an overview of the vLLM architecture.
 

docs/design/automatic_prefix_caching.md

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-title: Automatic Prefix Caching
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+# 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

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-title: Integration with HuggingFace
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+# Integration with HuggingFace
 
 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

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-title: vLLM Paged Attention
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+# vLLM 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

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-title: Multi-Modal Data Processing
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+# Multi-Modal Data Processing
 
 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.
 

docs/design/plugin_system.md

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-title: vLLM's Plugin System
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+# vLLM's 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.