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

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

docs/deployment/docker.md

@@ -1,7 +1,6 @@
 ---
 title: Using Docker
 ---
-[](){ #deployment-docker }
 
 [](){ #deployment-docker-pre-built-image }
 
@@ -32,7 +31,7 @@ podman run --gpus all \
   --model mistralai/Mistral-7B-v0.1
 ```
 
-You can add any other [engine-args][engine-args] you need after the image tag (`vllm/vllm-openai:latest`).
+You can add any other [engine-args](../configuration/engine_args.md) you need after the image tag (`vllm/vllm-openai:latest`).
 
 !!! note
     You can either use the `ipc=host` flag or `--shm-size` flag to allow the

docs/deployment/frameworks/anything-llm.md

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 title: Anything LLM
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-[](){ #deployment-anything-llm }
 
 [Anything LLM](https://github.com/Mintplex-Labs/anything-llm) is a full-stack application that enables you to turn any document, resource, or piece of content into context that any LLM can use as references during chatting.
 

docs/deployment/frameworks/autogen.md

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 title: AutoGen
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-[](){ #deployment-autogen }
 
 [AutoGen](https://github.com/microsoft/autogen) is a framework for creating multi-agent AI applications that can act autonomously or work alongside humans.
 

docs/deployment/frameworks/bentoml.md

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 title: BentoML
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-[](){ #deployment-bentoml }
 
 [BentoML](https://github.com/bentoml/BentoML) allows you to deploy a large language model (LLM) server with vLLM as the backend, which exposes OpenAI-compatible endpoints. You can serve the model locally or containerize it as an OCI-compliant image and deploy it on Kubernetes.
 

docs/deployment/frameworks/cerebrium.md

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 title: Cerebrium
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-[](){ #deployment-cerebrium }
 
 <p align="center">
     <img src="https://i.ibb.co/hHcScTT/Screenshot-2024-06-13-at-10-14-54.png" alt="vLLM_plus_cerebrium"/>

docs/deployment/frameworks/chatbox.md

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 title: Chatbox
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-[](){ #deployment-chatbox }
 
 [Chatbox](https://github.com/chatboxai/chatbox) is a desktop client for LLMs, available on Windows, Mac, Linux.
 

docs/deployment/frameworks/dify.md

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 title: Dify
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-[](){ #deployment-dify }
 
 [Dify](https://github.com/langgenius/dify) is an open-source LLM app development platform. Its intuitive interface combines agentic AI workflow, RAG pipeline, agent capabilities, model management, observability features, and more, allowing you to quickly move from prototype to production.
 

docs/deployment/frameworks/dstack.md

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 title: dstack
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-[](){ #deployment-dstack }
 
 <p align="center">
     <img src="https://i.ibb.co/71kx6hW/vllm-dstack.png" alt="vLLM_plus_dstack"/>

docs/deployment/frameworks/haystack.md

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 title: Haystack
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-[](){ #deployment-haystack }
 
 # Haystack
 

docs/deployment/frameworks/helm.md

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 title: Helm
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-[](){ #deployment-helm }
 
 A Helm chart to deploy vLLM for Kubernetes
 

docs/deployment/frameworks/litellm.md

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 title: LiteLLM
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-[](){ #deployment-litellm }
 
 [LiteLLM](https://github.com/BerriAI/litellm) call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]
 

docs/deployment/frameworks/lobe-chat.md

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 title: Lobe Chat
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-[](){ #deployment-lobe-chat }
 
 [Lobe Chat](https://github.com/lobehub/lobe-chat) is an open-source, modern-design ChatGPT/LLMs UI/Framework.
 

docs/deployment/frameworks/lws.md

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 title: LWS
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-[](){ #deployment-lws }
 
 LeaderWorkerSet (LWS) is a Kubernetes API that aims to address common deployment patterns of AI/ML inference workloads.
 A major use case is for multi-host/multi-node distributed inference.

docs/deployment/frameworks/modal.md

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 title: Modal
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-[](){ #deployment-modal }
 
 vLLM can be run on cloud GPUs with [Modal](https://modal.com), a serverless computing platform designed for fast auto-scaling.
 

docs/deployment/frameworks/open-webui.md

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 title: Open WebUI
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-[](){ #deployment-open-webui }
 
 1. Install the [Docker](https://docs.docker.com/engine/install/)
 

docs/deployment/frameworks/retrieval_augmented_generation.md

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 title: Retrieval-Augmented Generation
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-[](){ #deployment-retrieval-augmented-generation }
 
 [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.
 

docs/deployment/frameworks/skypilot.md

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 title: SkyPilot
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-[](){ #deployment-skypilot }
 
 <p align="center">
   <img src="https://imgur.com/yxtzPEu.png" alt="vLLM"/>

docs/deployment/frameworks/streamlit.md

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 title: Streamlit
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-[](){ #deployment-streamlit }
 
 [Streamlit](https://github.com/streamlit/streamlit) lets you transform Python scripts into interactive web apps in minutes, instead of weeks. Build dashboards, generate reports, or create chat apps.
 

docs/deployment/frameworks/triton.md

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 title: NVIDIA Triton
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-[](){ #deployment-triton }
 
 The [Triton Inference Server](https://github.com/triton-inference-server) hosts a tutorial demonstrating how to quickly deploy a simple [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) model using vLLM. Please see [Deploying a vLLM model in Triton](https://github.com/triton-inference-server/tutorials/blob/main/Quick_Deploy/vLLM/README.md#deploying-a-vllm-model-in-triton) for more details.

docs/deployment/integrations/kserve.md

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 title: KServe
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-[](){ #deployment-kserve }
 
 vLLM can be deployed with [KServe](https://github.com/kserve/kserve) on Kubernetes for highly scalable distributed model serving.
 

docs/deployment/integrations/kubeai.md

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 title: KubeAI
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-[](){ #deployment-kubeai }
 
 [KubeAI](https://github.com/substratusai/kubeai) is a Kubernetes operator that enables you to deploy and manage AI models on Kubernetes. It provides a simple and scalable way to deploy vLLM in production. Functionality such as scale-from-zero, load based autoscaling, model caching, and much more is provided out of the box with zero external dependencies.
 

docs/deployment/integrations/llamastack.md

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 title: Llama Stack
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-[](){ #deployment-llamastack }
 
 vLLM is also available via [Llama Stack](https://github.com/meta-llama/llama-stack) .
 

docs/deployment/integrations/llmaz.md

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 title: llmaz
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-[](){ #deployment-llmaz }
 
 [llmaz](https://github.com/InftyAI/llmaz) is an easy-to-use and advanced inference platform for large language models on Kubernetes, aimed for production use. It uses vLLM as the default model serving backend.
 

docs/deployment/integrations/production-stack.md

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 title: Production stack
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-[](){ #deployment-production-stack }
 
 Deploying vLLM on Kubernetes is a scalable and efficient way to serve machine learning models. This guide walks you through deploying vLLM using the [vLLM production stack](https://github.com/vllm-project/production-stack). Born out of a Berkeley-UChicago collaboration, [vLLM production stack](https://github.com/vllm-project/production-stack) is an officially released, production-optimized codebase under the [vLLM project](https://github.com/vllm-project), designed for LLM deployment with:
 

docs/deployment/k8s.md

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 title: Using Kubernetes
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-[](){ #deployment-k8s }
 
 Deploying vLLM on Kubernetes is a scalable and efficient way to serve machine learning models. This guide walks you through deploying vLLM using native Kubernetes.
 

docs/deployment/nginx.md

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