[Docs] Replace all explicit anchors with real links (#27087)

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

docs/models/generative_models.md

@@ -140,5 +140,5 @@ outputs = llm.chat(conversation, chat_template=custom_template)
 
 Our [OpenAI-Compatible Server](../serving/openai_compatible_server.md) provides endpoints that correspond to the offline APIs:
 
-- [Completions API][completions-api] is similar to `LLM.generate` but only accepts text.
-- [Chat API][chat-api]  is similar to `LLM.chat`, accepting both text and [multi-modal inputs](../features/multimodal_inputs.md) for models with a chat template.
+- [Completions API](../serving/openai_compatible_server.md#completions-api) is similar to `LLM.generate` but only accepts text.
+- [Chat API](../serving/openai_compatible_server.md#chat-api)  is similar to `LLM.chat`, accepting both text and [multi-modal inputs](../features/multimodal_inputs.md) for models with a chat template.

docs/models/pooling_models.md

@@ -185,10 +185,10 @@ print(f"Data: {data!r}")
 
 Our [OpenAI-Compatible Server](../serving/openai_compatible_server.md) provides endpoints that correspond to the offline APIs:
 
-- [Pooling API][pooling-api] is similar to `LLM.encode`, being applicable to all types of pooling models.
-- [Embeddings API][embeddings-api] is similar to `LLM.embed`, accepting both text and [multi-modal inputs](../features/multimodal_inputs.md) for embedding models.
-- [Classification API][classification-api] is similar to `LLM.classify` and is applicable to sequence classification models.
-- [Score API][score-api] is similar to `LLM.score` for cross-encoder models.
+- [Pooling API](../serving/openai_compatible_server.md#pooling-api) is similar to `LLM.encode`, being applicable to all types of pooling models.
+- [Embeddings API](../serving/openai_compatible_server.md#embeddings-api) is similar to `LLM.embed`, accepting both text and [multi-modal inputs](../features/multimodal_inputs.md) for embedding models.
+- [Classification API](../serving/openai_compatible_server.md#classification-api) is similar to `LLM.classify` and is applicable to sequence classification models.
+- [Score API](../serving/openai_compatible_server.md#score-api) is similar to `LLM.score` for cross-encoder models.
 
 ## Matryoshka Embeddings
 

docs/models/supported_models.md

@@ -11,9 +11,7 @@ Alongside each architecture, we include some popular models that use it.
 
 If vLLM natively supports a model, its implementation can be found in [vllm/model_executor/models](../../vllm/model_executor/models).
 
-These models are what we list in [supported-text-models][supported-text-models] and [supported-mm-models][supported-mm-models].
-
-[](){ #transformers-backend }
+These models are what we list in [supported text models](#list-of-text-only-language-models) and [supported multimodal models](#list-of-multimodal-language-models).
 
 ### Transformers
 
@@ -60,7 +58,7 @@ For a model to be compatible with the Transformers backend for vLLM it must:
 - be a Transformers compatible custom model (see [Transformers - Customizing models](https://huggingface.co/docs/transformers/en/custom_models)):
     - The model directory must have the correct structure (e.g. `config.json` is present).
     - `config.json` must contain `auto_map.AutoModel`.
-- be a Transformers backend for vLLM compatible model (see [writing-custom-models][writing-custom-models]):
+- be a Transformers backend for vLLM compatible model (see [Writing custom models](#writing-custom-models)):
     - Customisation should be done in the base model (e.g. in `MyModel`, not `MyModelForCausalLM`).
 
 If the compatible model is:
@@ -70,8 +68,6 @@ If the compatible model is:
 
 This means that, with the Transformers backend for vLLM, new models can be used before they are officially supported in Transformers or vLLM!
 
-[](){ #writing-custom-models }
-
 #### Writing custom models
 
 This section details the necessary modifications to make to a Transformers compatible custom model that make it compatible with the Transformers backend for vLLM. (We assume that a Transformers compatible custom model has already been created, see [Transformers - Customizing models](https://huggingface.co/docs/transformers/en/custom_models)).
@@ -164,7 +160,7 @@ To determine whether a given model is natively supported, you can check the `con
 If the `"architectures"` field contains a model architecture listed below, then it should be natively supported.
 
 Models do not _need_ to be natively supported to be used in vLLM.
-The [Transformers backend][transformers-backend] enables you to run models directly using their Transformers implementation (or even remote code on the Hugging Face Model Hub!).
+The [Transformers backend](#transformers) enables you to run models directly using their Transformers implementation (or even remote code on the Hugging Face Model Hub!).
 
 !!! tip
     The easiest way to check if your model is really supported at runtime is to run the program below:
@@ -306,8 +302,6 @@ output = llm.encode("Hello, my name is")
 print(output)
 ```
 
-[](){ #feature-status-legend }
-
 ## Feature Status Legend
 
 - ✅︎ indicates that the feature is supported for the model.
@@ -316,8 +310,6 @@ print(output)
 
 - ⚠️ indicates that the feature is available but may have known issues or limitations.
 
-[](){ #supported-text-models }
-
 ## List of Text-only Language Models
 
 ### Generative Models
@@ -583,8 +575,6 @@ These models primarily support the [`LLM.encode`](./pooling_models.md#llmencode)
 !!! note
     Named Entity Recognition (NER) usage, please refer to [examples/offline_inference/pooling/ner.py](../../examples/offline_inference/pooling/ner.py), [examples/online_serving/pooling/ner_client.py](../../examples/online_serving/pooling/ner_client.py).
 
-[](){ #supported-mm-models }
-
 ## List of Multimodal Language Models
 
 The following modalities are supported depending on the model: