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

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

Author: hmellor

docs/README.md

@@ -48,4 +48,4 @@ For more information, check out the following:
 - [vLLM announcing blog post](https://vllm.ai) (intro to PagedAttention)
 - [vLLM paper](https://arxiv.org/abs/2309.06180) (SOSP 2023)
 - [How continuous batching enables 23x throughput in LLM inference while reducing p50 latency](https://www.anyscale.com/blog/continuous-batching-llm-inference) by Cade Daniel et al.
-- [vLLM Meetups][meetups]
+- [vLLM Meetups](community/meetups.md)

docs/api/README.md

@@ -64,7 +64,7 @@ vLLM provides experimental support for multi-modal models through the [vllm.mult
 Multi-modal inputs can be passed alongside text and token prompts to [supported models][supported-mm-models]
 via the `multi_modal_data` field in [vllm.inputs.PromptType][].
 
-Looking to add your own multi-modal model? Please follow the instructions listed [here][supports-multimodal].
+Looking to add your own multi-modal model? Please follow the instructions listed [here](../contributing/model/multimodal.md).
 
 - [vllm.multimodal.MULTIMODAL_REGISTRY][]
 

docs/community/contact_us.md

@@ -1,6 +1,5 @@
 ---
 title: Contact Us
 ---
-[](){ #contactus }
 
 --8<-- "README.md:contact-us"

docs/community/meetups.md

@@ -1,7 +1,6 @@
 ---
 title: Meetups
 ---
-[](){ #meetups }
 
 We host regular meetups in San Francisco Bay Area every 2 months. We will share the project updates from the vLLM team and have guest speakers from the industry to share their experience and insights. Please find the materials of our previous meetups below:
 

docs/configuration/conserving_memory.md

@@ -33,7 +33,7 @@ Quantized models take less memory at the cost of lower precision.
 Statically quantized models can be downloaded from HF Hub (some popular ones are available at [Red Hat AI](https://huggingface.co/RedHatAI))
 and used directly without extra configuration.
 
-Dynamic quantization is also supported via the `quantization` option -- see [here][quantization-index] for more details.
+Dynamic quantization is also supported via the `quantization` option -- see [here](../features/quantization/README.md) for more details.
 
 ## Context length and batch size
 

docs/configuration/engine_args.md

@@ -1,12 +1,11 @@
 ---
 title: Engine Arguments
 ---
-[](){ #engine-args }
 
 Engine arguments control the behavior of the vLLM engine.
 
-- For [offline inference][offline-inference], they are part of the arguments to [LLM][vllm.LLM] class.
-- For [online serving][serving-openai-compatible-server], they are part of the arguments to `vllm serve`.
+- For [offline inference](../serving/offline_inference.md), they are part of the arguments to [LLM][vllm.LLM] class.
+- For [online serving](../serving/openai_compatible_server.md), they are part of the arguments to `vllm serve`.
 
 You can look at [EngineArgs][vllm.engine.arg_utils.EngineArgs] and [AsyncEngineArgs][vllm.engine.arg_utils.AsyncEngineArgs] to see the available engine arguments.
 

docs/configuration/model_resolution.md

@@ -20,4 +20,4 @@ model = LLM(
 )
 ```
 
-Our [list of supported models][supported-models] shows the model architectures that are recognized by vLLM.
+Our [list of supported models](../models/supported_models.md) shows the model architectures that are recognized by vLLM.

docs/configuration/serve_args.md

@@ -1,7 +1,6 @@
 ---
 title: Server Arguments
 ---
-[](){ #serve-args }
 
 The `vllm serve` command is used to launch the OpenAI-compatible server.
 
@@ -13,7 +12,7 @@ To see the available CLI arguments, run `vllm serve --help`!
 ## Configuration file
 
 You can load CLI arguments via a [YAML](https://yaml.org/) config file.
-The argument names must be the long form of those outlined [above][serve-args].
+The argument names must be the long form of those outlined [above](serve_args.md).
 
 For example:
 

docs/contributing/benchmarks.md

@@ -1,7 +1,6 @@
 ---
 title: Benchmark Suites
 ---
-[](){ #benchmarks }
 
 vLLM contains two sets of benchmarks:
 

docs/contributing/dockerfile/dockerfile.md

@@ -1,7 +1,7 @@
 # Dockerfile
 
 We provide a <gh-file:docker/Dockerfile> to construct the image for running an OpenAI compatible server with vLLM.
-More information about deploying with Docker can be found [here][deployment-docker].
+More information about deploying with Docker can be found [here](../../deployment/docker.md).
 
 Below is a visual representation of the multi-stage Dockerfile. The build graph contains the following nodes: