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/features/quantization/quantized_kvcache.md

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-title: Quantized KV Cache
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+# Quantized KV Cache
 
 ## FP8 KV Cache
 

docs/features/quantization/quark.md

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-title: AMD Quark
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+# AMD Quark
 
 Quantization can effectively reduce memory and bandwidth usage, accelerate computation and improve
 throughput while with minimal accuracy loss. vLLM can leverage [Quark](https://quark.docs.amd.com/latest/),

docs/features/quantization/supported_hardware.md

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-title: Supported Hardware
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+# Supported Hardware
 
 The table below shows the compatibility of various quantization implementations with different hardware platforms in vLLM:
 

docs/features/reasoning_outputs.md

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-title: Reasoning Outputs
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+# Reasoning Outputs
 
 vLLM offers support for reasoning models like [DeepSeek R1](https://huggingface.co/deepseek-ai/DeepSeek-R1), which are designed to generate outputs containing both reasoning steps and final conclusions.
 

docs/features/spec_decode.md

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-title: Speculative Decoding
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+# Speculative Decoding
 
 !!! warning
     Please note that speculative decoding in vLLM is not yet optimized and does

docs/features/structured_outputs.md

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-title: Structured Outputs
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+# Structured Outputs
 
 vLLM supports the generation of structured outputs using
 [xgrammar](https://github.com/mlc-ai/xgrammar) or

docs/getting_started/installation/README.md

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-title: Installation
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+# Installation
 
 vLLM supports the following hardware platforms:
 

docs/getting_started/quickstart.md

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-title: Quickstart
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+# Quickstart
 
 This guide will help you quickly get started with vLLM to perform:
 

docs/models/extensions/runai_model_streamer.md

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-title: Loading models with Run:ai Model Streamer
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+# Loading models with Run:ai Model Streamer
 
 Run:ai Model Streamer is a library to read tensors in concurrency, while streaming it to GPU memory.
 Further reading can be found in [Run:ai Model Streamer Documentation](https://github.com/run-ai/runai-model-streamer/blob/master/docs/README.md).

docs/models/extensions/tensorizer.md

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-title: Loading models with CoreWeave's Tensorizer
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+# Loading models with CoreWeave's Tensorizer
 
 vLLM supports loading models with [CoreWeave's Tensorizer](https://docs.coreweave.com/coreweave-machine-learning-and-ai/inference/tensorizer).
 vLLM model tensors that have been serialized to disk, an HTTP/HTTPS endpoint, or S3 endpoint can be deserialized

docs/models/generative_models.md

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-title: Generative Models
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+# Generative Models
 
 vLLM provides first-class support for generative models, which covers most of LLMs.
 

docs/models/hardware_supported_models/tpu.md

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-title: TPU
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+# TPU
 
 # TPU Supported Models
 ## Text-only Language Models

docs/models/pooling_models.md

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-title: Pooling Models
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+# Pooling Models
 
 vLLM also supports pooling models, including embedding, reranking and reward models.
 

docs/models/supported_models.md

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-title: Supported Models
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+# Supported Models
 
 vLLM supports [generative](./generative_models.md) and [pooling](./pooling_models.md) models across various tasks.
 If a model supports more than one task, you can set the task via the `--task` argument.

docs/serving/distributed_serving.md

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-title: Distributed Inference and Serving
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+# Distributed Inference and Serving
 
 ## How to decide the distributed inference strategy?
 

docs/serving/integrations/langchain.md

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-title: LangChain
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+# LangChain
 
 vLLM is also available via [LangChain](https://github.com/langchain-ai/langchain) .
 

docs/serving/integrations/llamaindex.md

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-title: LlamaIndex
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+# LlamaIndex
 
 vLLM is also available via [LlamaIndex](https://github.com/run-llama/llama_index) .
 

docs/serving/offline_inference.md

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-title: Offline Inference
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+# Offline Inference
 
 Offline inference is possible in your own code using vLLM's [`LLM`][vllm.LLM] class.
 
@@ -23,7 +21,7 @@ The available APIs depend on the model type:
 !!! info
     [API Reference][offline-inference-api]
 
-### Ray Data LLM API
+## Ray Data LLM API
 
 Ray Data LLM is an alternative offline inference API that uses vLLM as the underlying engine.
 This API adds several batteries-included capabilities that simplify large-scale, GPU-efficient inference:

docs/serving/openai_compatible_server.md

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-title: OpenAI-Compatible Server
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+# OpenAI-Compatible Server
 
 vLLM provides an HTTP server that implements OpenAI's [Completions API](https://platform.openai.com/docs/api-reference/completions), [Chat API](https://platform.openai.com/docs/api-reference/chat), and more! This functionality lets you serve models and interact with them using an HTTP client.
 

docs/usage/faq.md

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-title: Frequently Asked Questions
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+# Frequently Asked Questions
 
 > Q: How can I serve multiple models on a single port using the OpenAI API?