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

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

docs/contributing/benchmarks.md

@@ -7,8 +7,8 @@ toc_depth: 4
 vLLM provides comprehensive benchmarking tools for performance testing and evaluation:
 
 - **[Benchmark CLI]**: `vllm bench` CLI tools and specialized benchmark scripts for interactive performance testing
-- **[Performance benchmarks][performance-benchmarks]**: Automated CI benchmarks for development
-- **[Nightly benchmarks][nightly-benchmarks]**: Comparative benchmarks against alternatives
+- **[Performance benchmarks](#performance-benchmarks)**: Automated CI benchmarks for development
+- **[Nightly benchmarks](#nightly-benchmarks)**: Comparative benchmarks against alternatives
 
 [Benchmark CLI]: #benchmark-cli
 
@@ -924,8 +924,6 @@ throughput numbers correctly is also adjusted.
 
 </details>
 
-[](){ #performance-benchmarks }
-
 ## Performance Benchmarks
 
 The performance benchmarks are used for development to confirm whether new changes improve performance under various workloads. They are triggered on every commit with both the `perf-benchmarks` and `ready` labels, and when a PR is merged into vLLM.
@@ -988,8 +986,6 @@ The benchmarking currently runs on a predefined set of models configured in the
 
 All continuous benchmarking results are automatically published to the public [vLLM Performance Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm).
 
-[](){ #nightly-benchmarks }
-
 ## Nightly Benchmarks
 
 These compare vLLM's performance against alternatives (`tgi`, `trt-llm`, and `lmdeploy`) when there are major updates of vLLM (e.g., bumping up to a new version). They are primarily intended for consumers to evaluate when to choose vLLM over other options and are triggered on every commit with both the `perf-benchmarks` and `nightly-benchmarks` labels.

docs/contributing/model/README.md

@@ -1,7 +1,7 @@
 # Summary
 
 !!! 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!
+    Many decoder language models can now be automatically loaded using the [Transformers backend](../../models/supported_models.md#transformers) 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](../../features/README.md#compatibility-matrix) to optimize their performance.
 

docs/contributing/model/registration.md

@@ -8,7 +8,7 @@ This page provides detailed instructions on how to do so.
 
 ## Built-in models
 
-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].
+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](../../getting_started/installation/gpu.md#build-wheel-from-source).
 This gives you the ability to modify the codebase and test your model.
 
 After you have implemented your model (see [tutorial](basic.md)), put it into the [vllm/model_executor/models](../../../vllm/model_executor/models) directory.

docs/contributing/model/tests.md

@@ -39,8 +39,6 @@ For [generative models](../../models/generative_models.md), there are two levels
 
 For [pooling models](../../models/pooling_models.md), we simply check the cosine similarity, as defined in [tests/models/utils.py](../../../tests/models/utils.py).
 
-[](){ #mm-processing-tests }
-
 ### Multi-modal processing
 
 #### Common tests