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

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

docs/contributing/model/README.md

@@ -1,12 +1,11 @@
 ---
 title: Summary
 ---
-[](){ #new-model }
 
 !!! important
     Many decoder language models can now be automatically loaded using the [Transformers backend][transformers-backend] without having to implement them in vLLM. See if `vllm serve <model>` works first!
 
-vLLM models are specialized [PyTorch](https://pytorch.org/) models that take advantage of various [features][compatibility-matrix] to optimize their performance.
+vLLM models are specialized [PyTorch](https://pytorch.org/) models that take advantage of various [features](../../features/compatibility_matrix.md) to optimize their performance.
 
 The complexity of integrating a model into vLLM depends heavily on the model's architecture.
 The process is considerably straightforward if the model shares a similar architecture with an existing model in vLLM.

docs/contributing/model/basic.md

@@ -1,7 +1,6 @@
 ---
 title: Basic Model
 ---
-[](){ #new-model-basic }
 
 This guide walks you through the steps to implement a basic vLLM model.
 
@@ -108,7 +107,7 @@ This method should load the weights from the HuggingFace's checkpoint file and a
 
 ## 5. Register your model
 
-See [this page][new-model-registration] for instructions on how to register your new model to be used by vLLM.
+See [this page](registration.md) for instructions on how to register your new model to be used by vLLM.
 
 ## Frequently Asked Questions
 

docs/contributing/model/multimodal.md

@@ -1,13 +1,12 @@
 ---
 title: Multi-Modal Support
 ---
-[](){ #supports-multimodal }
 
-This document walks you through the steps to extend a basic model so that it accepts [multi-modal inputs][multimodal-inputs].
+This document walks you through the steps to extend a basic model so that it accepts [multi-modal inputs](../../features/multimodal_inputs.md).
 
 ## 1. Update the base vLLM model
 
-It is assumed that you have already implemented the model in vLLM according to [these steps][new-model-basic].
+It is assumed that you have already implemented the model in vLLM according to [these steps](basic.md).
 Further update the model as follows:
 
 - Implement [get_placeholder_str][vllm.model_executor.models.interfaces.SupportsMultiModal.get_placeholder_str] to define the placeholder string which is used to represent the multi-modal item in the text prompt. This should be consistent with the chat template of the model.
@@ -483,7 +482,7 @@ Afterwards, create a subclass of [BaseMultiModalProcessor][vllm.multimodal.proce
 to fill in the missing details about HF processing.
 
 !!! info
-    [Multi-Modal Data Processing][mm-processing]
+    [Multi-Modal Data Processing](../../design/mm_processing.md)
 
 ### Multi-modal fields
 
@@ -846,7 +845,7 @@ Examples:
 
 ### Handling prompt updates unrelated to multi-modal data
 
-[_get_prompt_updates][vllm.multimodal.processing.BaseMultiModalProcessor._get_prompt_updates] assumes that each application of prompt update corresponds to one multi-modal item. If the HF processor performs additional processing regardless of how many multi-modal items there are, you should override [_apply_hf_processor_tokens_only][vllm.multimodal.processing.BaseMultiModalProcessor._apply_hf_processor_tokens_only] so that the processed token inputs are consistent with the result of applying the HF processor on text inputs. This is because token inputs bypass the HF processor according to [our design][mm-processing].
+[_get_prompt_updates][vllm.multimodal.processing.BaseMultiModalProcessor._get_prompt_updates] assumes that each application of prompt update corresponds to one multi-modal item. If the HF processor performs additional processing regardless of how many multi-modal items there are, you should override [_apply_hf_processor_tokens_only][vllm.multimodal.processing.BaseMultiModalProcessor._apply_hf_processor_tokens_only] so that the processed token inputs are consistent with the result of applying the HF processor on text inputs. This is because token inputs bypass the HF processor according to [our design](../../design/mm_processing.md).
 
 Examples:
 

docs/contributing/model/registration.md

@@ -1,10 +1,9 @@
 ---
 title: Registering a Model
 ---
-[](){ #new-model-registration }
 
 vLLM relies on a model registry to determine how to run each model.
-A list of pre-registered architectures can be found [here][supported-models].
+A list of pre-registered architectures can be found [here](../../models/supported_models.md).
 
 If your model is not on this list, you must register it to vLLM.
 This page provides detailed instructions on how to do so.
@@ -14,16 +13,16 @@ This page provides detailed instructions on how to do so.
 To add a model directly to the vLLM library, start by forking our [GitHub repository](https://github.com/vllm-project/vllm) and then [build it from source][build-from-source].
 This gives you the ability to modify the codebase and test your model.
 
-After you have implemented your model (see [tutorial][new-model-basic]), put it into the <gh-dir:vllm/model_executor/models> directory.
+After you have implemented your model (see [tutorial](basic.md)), put it into the <gh-dir:vllm/model_executor/models> directory.
 Then, add your model class to `_VLLM_MODELS` in <gh-file:vllm/model_executor/models/registry.py> so that it is automatically registered upon importing vLLM.
-Finally, update our [list of supported models][supported-models] to promote your model!
+Finally, update our [list of supported models](../../models/supported_models.md) to promote your model!
 
 !!! important
     The list of models in each section should be maintained in alphabetical order.
 
 ## Out-of-tree models
 
-You can load an external model [using a plugin][plugin-system] without modifying the vLLM codebase.
+You can load an external model [using a plugin](../../design/plugin_system.md) without modifying the vLLM codebase.
 
 To register the model, use the following code:
 
@@ -51,4 +50,4 @@ def register():
 
 !!! important
     If your model is a multimodal model, ensure the model class implements the [SupportsMultiModal][vllm.model_executor.models.interfaces.SupportsMultiModal] interface.
-    Read more about that [here][supports-multimodal].
+    Read more about that [here](multimodal.md).

docs/contributing/model/tests.md

@@ -1,7 +1,6 @@
 ---
 title: Unit Testing
 ---
-[](){ #new-model-tests }
 
 This page explains how to write unit tests to verify the implementation of your model.