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

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

docs/deployment/integrations/kserve.md

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 title: KServe
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 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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 [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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 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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 [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: