[Docs] Reduce custom syntax used in docs (#27009)

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

docs/contributing/README.md

@@ -22,7 +22,7 @@ Unsure on where to start? Check out the following links for tasks to work on:
 
 ## License
 
-See <gh-file:LICENSE>.
+See [LICENSE](../../LICENSE).
 
 ## Developing
 
@@ -54,7 +54,7 @@ For more details about installing from source and installing for other hardware,
 For an optimized workflow when iterating on C++/CUDA kernels, see the [Incremental Compilation Workflow](./incremental_build.md) for recommendations.
 
 !!! tip
-    vLLM is compatible with Python versions 3.10 to 3.13. However, vLLM's default [Dockerfile](gh-file:docker/Dockerfile) ships with Python 3.12 and tests in CI (except `mypy`) are run with Python 3.12.
+    vLLM is compatible with Python versions 3.10 to 3.13. However, vLLM's default [Dockerfile](../../docker/Dockerfile) ships with Python 3.12 and tests in CI (except `mypy`) are run with Python 3.12.
 
     Therefore, we recommend developing with Python 3.12 to minimise the chance of your local environment clashing with our CI environment.
 
@@ -88,7 +88,7 @@ vLLM's `pre-commit` hooks will now run automatically every time you commit.
 
 ### Documentation
 
-MkDocs is a fast, simple and downright gorgeous static site generator that's geared towards building project documentation. Documentation source files are written in Markdown, and configured with a single YAML configuration file, <gh-file:mkdocs.yaml>.
+MkDocs is a fast, simple and downright gorgeous static site generator that's geared towards building project documentation. Documentation source files are written in Markdown, and configured with a single YAML configuration file, [mkdocs.yaml](../../mkdocs.yaml).
 
 Get started with:
 
@@ -152,7 +152,7 @@ pytest -s -v tests/test_logger.py
 If you encounter a bug or have a feature request, please [search existing issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue) first to see if it has already been reported. If not, please [file a new issue](https://github.com/vllm-project/vllm/issues/new/choose), providing as much relevant information as possible.
 
 !!! important
-    If you discover a security vulnerability, please follow the instructions [here](gh-file:SECURITY.md#reporting-a-vulnerability).
+    If you discover a security vulnerability, please follow the instructions [here](../../SECURITY.md).
 
 ## Pull Requests & Code Reviews
 
@@ -162,7 +162,7 @@ code quality and improve the efficiency of the review process.
 
 ### DCO and Signed-off-by
 
-When contributing changes to this project, you must agree to the <gh-file:DCO>.
+When contributing changes to this project, you must agree to the [DCO](../../DCO).
 Commits must include a `Signed-off-by:` header which certifies agreement with
 the terms of the DCO.
 

docs/contributing/benchmarks.md

@@ -822,7 +822,7 @@ you should set `--endpoint /v1/embeddings` to use the Embeddings API. The backen
 - CLIP: `--backend openai-embeddings-clip`
 - VLM2Vec: `--backend openai-embeddings-vlm2vec`
 
-For other models, please add your own implementation inside <gh-file:vllm/benchmarks/lib/endpoint_request_func.py> to match the expected instruction format.
+For other models, please add your own implementation inside [vllm/benchmarks/lib/endpoint_request_func.py](../../vllm/benchmarks/lib/endpoint_request_func.py) to match the expected instruction format.
 
 You can use any text or multi-modal dataset to benchmark the model, as long as the model supports it.
 For example, you can use ShareGPT and VisionArena to benchmark vision-language embeddings.
@@ -962,7 +962,7 @@ For more results visualization, check the [visualizing the results](https://gith
 
 The latest performance results are hosted on the public [vLLM Performance Dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm).
 
-More information on the performance benchmarks and their parameters can be found in [Benchmark README](https://github.com/intel-ai-tce/vllm/blob/more_cpu_models/.buildkite/nightly-benchmarks/README.md) and [performance benchmark description](gh-file:.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md).
+More information on the performance benchmarks and their parameters can be found in [Benchmark README](https://github.com/intel-ai-tce/vllm/blob/more_cpu_models/.buildkite/nightly-benchmarks/README.md) and [performance benchmark description](../../.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md).
 
 ### Continuous Benchmarking
 
@@ -996,4 +996,4 @@ These compare vLLM's performance against alternatives (`tgi`, `trt-llm`, and `lm
 
 The latest nightly benchmark results are shared in major release blog posts such as [vLLM v0.6.0](https://blog.vllm.ai/2024/09/05/perf-update.html).
 
-More information on the nightly benchmarks and their parameters can be found [here](gh-file:.buildkite/nightly-benchmarks/nightly-descriptions.md).
+More information on the nightly benchmarks and their parameters can be found [here](../../.buildkite/nightly-benchmarks/nightly-descriptions.md).

docs/contributing/ci/failures.md

@@ -64,7 +64,7 @@ Download the full log file from Buildkite locally.
 
 Strip timestamps and colorization:
 
-<gh-file:.buildkite/scripts/ci-clean-log.sh>
+[.buildkite/scripts/ci-clean-log.sh](../../../.buildkite/scripts/ci-clean-log.sh)
 
 ```bash
 ./ci-clean-log.sh ci.log
@@ -87,7 +87,7 @@ tail -525 ci_build.log | wl-copy
 
 CI test failures may be flaky. Use a bash loop to run repeatedly:
 
-<gh-file:.buildkite/scripts/rerun-test.sh>
+[.buildkite/scripts/rerun-test.sh](../../../.buildkite/scripts/rerun-test.sh)
 
 ```bash
 ./rerun-test.sh tests/v1/engine/test_engine_core_client.py::test_kv_cache_events[True-tcp]

docs/contributing/ci/update_pytorch_version.md

@@ -5,7 +5,7 @@ release in CI/CD. It is standard practice to submit a PR to update the
 PyTorch version as early as possible when a new [PyTorch stable
 release](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-cadence) becomes available.
 This process is non-trivial due to the gap between PyTorch
-releases. Using <gh-pr:16859> as an example, this document outlines common steps to achieve this
+releases. Using <https://github.com/vllm-project/vllm/pull/16859> as an example, this document outlines common steps to achieve this
 update along with a list of potential issues and how to address them.
 
 ## Test PyTorch release candidates (RCs)
@@ -85,7 +85,7 @@ and timeout. Additionally, since vLLM's fastcheck pipeline runs in read-only mod
 it doesn't populate the cache, so re-running it to warm up the cache
 is ineffective.
 
-While ongoing efforts like [#17419](gh-issue:17419)
+While ongoing efforts like <https://github.com/vllm-project/vllm/issues/17419>
 address the long build time at its source, the current workaround is to set `VLLM_CI_BRANCH`
 to a custom branch provided by @khluu (`VLLM_CI_BRANCH=khluu/use_postmerge_q`)
 when manually triggering a build on Buildkite. This branch accomplishes two things:
@@ -138,5 +138,5 @@ to handle some platforms separately. The separation of requirements and Dockerfi
 for different platforms in vLLM CI/CD allows us to selectively choose
 which platforms to update. For instance, updating XPU requires the corresponding
 release from [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch) by Intel.
-While <gh-pr:16859> updated vLLM to PyTorch 2.7.0 on CPU, CUDA, and ROCm,
-<gh-pr:17444> completed the update for XPU.
+While <https://github.com/vllm-project/vllm/pull/16859> updated vLLM to PyTorch 2.7.0 on CPU, CUDA, and ROCm,
+<https://github.com/vllm-project/vllm/pull/17444> completed the update for XPU.

docs/contributing/dockerfile/dockerfile.md

@@ -1,6 +1,6 @@
 # Dockerfile
 
-We provide a <gh-file:docker/Dockerfile> to construct the image for running an OpenAI compatible server with vLLM.
+We provide a [docker/Dockerfile](../../../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.md).
 
 Below is a visual representation of the multi-stage Dockerfile. The build graph contains the following nodes:

docs/contributing/model/basic.md

@@ -5,7 +5,7 @@ This guide walks you through the steps to implement a basic vLLM model.
 ## 1. Bring your model code
 
 First, clone the PyTorch model code from the source repository.
-For instance, vLLM's [OPT model](gh-file:vllm/model_executor/models/opt.py) was adapted from
+For instance, vLLM's [OPT model](../../../vllm/model_executor/models/opt.py) was adapted from
 HuggingFace's [modeling_opt.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/opt/modeling_opt.py) file.
 
 !!! warning
@@ -83,7 +83,7 @@ def forward(
     Currently, vLLM supports the basic multi-head attention mechanism and its variant with rotary positional embeddings.
     If your model employs a different attention mechanism, you will need to implement a new attention layer in vLLM.
 
-For reference, check out our [Llama implementation](gh-file:vllm/model_executor/models/llama.py). vLLM already supports a large number of models. It is recommended to find a model similar to yours and adapt it to your model's architecture. Check out <gh-dir:vllm/model_executor/models> for more examples.
+For reference, check out our [Llama implementation](../../../vllm/model_executor/models/llama.py). vLLM already supports a large number of models. It is recommended to find a model similar to yours and adapt it to your model's architecture. Check out [vllm/model_executor/models](../../../vllm/model_executor/models) for more examples.
 
 ## 3. (Optional) Implement tensor parallelism and quantization support
 
@@ -130,22 +130,22 @@ We consider 3 different scenarios:
 2. Models that combine Mamba layers (either Mamba-1 or Mamba-2) together with attention layers.
 3. Models that combine Mamba-like mechanisms (e.g., Linear Attention, ShortConv) together with attention layers.
 
-For case (1), we recommend looking at the implementation of [`MambaForCausalLM`](gh-file:vllm/model_executor/models/mamba.py) (for Mamba-1) or [`Mamba2ForCausalLM`](gh-file:vllm/model_executor/models/mamba2.py) (for Mamba-2) as a reference.
+For case (1), we recommend looking at the implementation of [`MambaForCausalLM`](../../../vllm/model_executor/models/mamba.py) (for Mamba-1) or [`Mamba2ForCausalLM`](../../../vllm/model_executor/models/mamba2.py) (for Mamba-2) as a reference.
 The model should inherit protocol `IsAttentionFree` and also implement class methods `get_mamba_state_dtype_from_config` and `get_mamba_state_shape_from_config` to calculate the state shapes and data types from the config.
-For the mamba layers themselves, please use the [`MambaMixer`](gh-file:vllm/model_executor/layers/mamba/mamba_mixer.py) (for Mamba-1) or [`MambaMixer2`](gh-file:vllm/model_executor/layers/mamba/mamba_mixer2.py) (for Mamba-2) classes.
+For the mamba layers themselves, please use the [`MambaMixer`](../../../vllm/model_executor/layers/mamba/mamba_mixer.py) (for Mamba-1) or [`MambaMixer2`](../../../vllm/model_executor/layers/mamba/mamba_mixer2.py) (for Mamba-2) classes.
 Please *do not* use the `MambaCacheManager` (deprecated in V1) or replicate any of the V0-specific code paths in the existing model implementations.
 V0-only classes and code will be removed in the very near future.
-The model should also be added to the `MODELS_CONFIG_MAP` dictionary in <gh-file:vllm/model_executor/models/config.py> to ensure that the runtime defaults are optimized.
+The model should also be added to the `MODELS_CONFIG_MAP` dictionary in [vllm/model_executor/models/config.py](../../../vllm/model_executor/models/config.py) to ensure that the runtime defaults are optimized.
 
-For case (2), we recommend using as a reference the implementation of [`JambaForCausalLM`](gh-file:vllm/model_executor/models/jamba.py) (for an example of a model that uses Mamba-1 and attention together) or [`BambaForCausalLM`](gh-file:vllm/model_executor/models/bamba.py) (for an example of a model that uses Mamba-2 and attention together).
+For case (2), we recommend using as a reference the implementation of [`JambaForCausalLM`](../../../vllm/model_executor/models/jamba.py) (for an example of a model that uses Mamba-1 and attention together) or [`BambaForCausalLM`](../../../vllm/model_executor/models/bamba.py) (for an example of a model that uses Mamba-2 and attention together).
 These models should follow the same instructions as case (1), but they should inherit protocol `IsHybrid` (instead of `IsAttentionFree`) and it is *not* necessary to add them to the `MODELS_CONFIG_MAP` (their runtime defaults will be inferred from the protocol).
 
-For case (3), we recommend looking at the implementation of [`MiniMaxText01ForCausalLM`](gh-file:vllm/model_executor/models/minimax_text_01.py) or [`Lfm2ForCausalLM`](gh-file:vllm/model_executor/models/lfm2.py) as a reference, which use custom "mamba-like" layers `MiniMaxText01LinearAttention` and `ShortConv` respectively.
+For case (3), we recommend looking at the implementation of [`MiniMaxText01ForCausalLM`](../../../vllm/model_executor/models/minimax_text_01.py) or [`Lfm2ForCausalLM`](../../../vllm/model_executor/models/lfm2.py) as a reference, which use custom "mamba-like" layers `MiniMaxText01LinearAttention` and `ShortConv` respectively.
 Please follow the same guidelines as case (2) for implementing these models.
 We use "mamba-like" to refer to layers that posses a state that is updated in-place, rather than being appended-to (like KV cache for attention).
 For implementing new custom mamba-like layers, one should inherit from `MambaBase` and implement the methods `get_state_dtype`, `get_state_shape` to calculate the data types and state shapes at runtime, as well as `mamba_type` and `get_attn_backend`.
 It is also necessary to implement the "attention meta-data" class which handles the meta-data that is common across all layers.
-Please see [`LinearAttentionMetadata`](gh-file:vllm/v1/attention/backends/linear_attn.py) or [`ShortConvAttentionMetadata`](gh-file:v1/attention/backends/short_conv_attn.py) for examples of this.
+Please see [`LinearAttentionMetadata`](../../../vllm/v1/attention/backends/linear_attn.py) or [`ShortConvAttentionMetadata`](../../../vllm/v1/attention/backends/short_conv_attn.py) for examples of this.
 Finally, if one wants to support torch compile and CUDA graphs, it necessary to wrap the call to the mamba-like layer inside a custom op and register it.
-Please see the calls to `direct_register_custom_op` in <gh-file:vllm/model_executor/models/minimax_text_01.py> or <gh-file:vllm/model_executor/layers/mamba/short_conv.py> for examples of this.
-The new custom op should then be added to the list `_attention_ops` in <gh-file:vllm/config/compilation.py> to ensure that piecewise CUDA graphs works as intended.
+Please see the calls to `direct_register_custom_op` in [vllm/model_executor/models/minimax_text_01.py](../../../vllm/model_executor/models/minimax_text_01.py) or [vllm/model_executor/layers/mamba/short_conv.py](../../../vllm/model_executor/layers/mamba/short_conv.py) for examples of this.
+The new custom op should then be added to the list `_attention_ops` in [vllm/config/compilation.py](../../../vllm/config/compilation.py) to ensure that piecewise CUDA graphs works as intended.

docs/contributing/model/multimodal.md

@@ -507,7 +507,7 @@ return a schema of the tensors outputted by the HF processor that are related to
     ```
 
     !!! note
-        Our [actual code](gh-file:vllm/model_executor/models/llava.py) additionally supports
+        Our [actual code](../../../vllm/model_executor/models/llava.py) additionally supports
         pre-computed image embeddings, which can be passed to be model via the `image_embeds` argument.
 
 === "With postprocessing: Fuyu"
@@ -569,7 +569,7 @@ return a schema of the tensors outputted by the HF processor that are related to
         ```
 
     !!! note
-        Our [actual code](gh-file:vllm/model_executor/models/fuyu.py) has special handling
+        Our [actual code](../../../vllm/model_executor/models/fuyu.py) has special handling
         for text-only inputs to prevent unnecessary warnings from HF processor.
 
     !!! note
@@ -828,8 +828,8 @@ Some HF processors directly insert feature tokens without replacing anything in
 
 Examples:
 
-- BLIP-2 (insert at start of prompt): <gh-file:vllm/model_executor/models/blip2.py>
-- Molmo (insert after `<|endoftext|>` token): <gh-file:vllm/model_executor/models/molmo.py>
+- BLIP-2 (insert at start of prompt): [vllm/model_executor/models/blip2.py](../../../vllm/model_executor/models/blip2.py)
+- Molmo (insert after `<|endoftext|>` token): [vllm/model_executor/models/molmo.py](../../../vllm/model_executor/models/molmo.py)
 
 ### Handling prompt updates unrelated to multi-modal data
 
@@ -837,9 +837,9 @@ Examples:
 
 Examples:
 
-- Chameleon (appends `sep_token`): <gh-file:vllm/model_executor/models/chameleon.py>
-- Fuyu (appends `boa_token`): <gh-file:vllm/model_executor/models/fuyu.py>
-- Molmo (applies chat template which is not defined elsewhere): <gh-file:vllm/model_executor/models/molmo.py>
+- Chameleon (appends `sep_token`): [vllm/model_executor/models/chameleon.py](../../../vllm/model_executor/models/chameleon.py)
+- Fuyu (appends `boa_token`): [vllm/model_executor/models/fuyu.py](../../../vllm/model_executor/models/fuyu.py)
+- Molmo (applies chat template which is not defined elsewhere): [vllm/model_executor/models/molmo.py](../../../vllm/model_executor/models/molmo.py)
 
 ### Custom HF processor
 
@@ -847,6 +847,6 @@ Some models don't define an HF processor class on HF Hub. In that case, you can
 
 Examples:
 
-- DeepSeek-VL2: <gh-file:vllm/model_executor/models/deepseek_vl2.py>
-- InternVL: <gh-file:vllm/model_executor/models/internvl.py>
-- Qwen-VL: <gh-file:vllm/model_executor/models/qwen_vl.py>
+- DeepSeek-VL2: [vllm/model_executor/models/deepseek_vl2.py](../../../vllm/model_executor/models/deepseek_vl2.py)
+- InternVL: [vllm/model_executor/models/internvl.py](../../../vllm/model_executor/models/internvl.py)
+- Qwen-VL: [vllm/model_executor/models/qwen_vl.py](../../../vllm/model_executor/models/qwen_vl.py)

docs/contributing/model/registration.md

@@ -11,8 +11,8 @@ 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](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.
+After you have implemented your model (see [tutorial](basic.md)), put it into the [vllm/model_executor/models](../../../vllm/model_executor/models) directory.
+Then, add your model class to `_VLLM_MODELS` in [vllm/model_executor/models/registry.py](../../../vllm/model_executor/models/registry.py) so that it is automatically registered upon importing vLLM.
 Finally, update our [list of supported models](../../models/supported_models.md) to promote your model!
 
 !!! important

docs/contributing/model/tests.md

@@ -9,7 +9,7 @@ Without them, the CI for your PR will fail.
 
 ### Model loading
 
-Include an example HuggingFace repository for your model in <gh-file:tests/models/registry.py>.
+Include an example HuggingFace repository for your model in [tests/models/registry.py](../../../tests/models/registry.py).
 This enables a unit test that loads dummy weights to ensure that the model can be initialized in vLLM.
 
 !!! important
@@ -26,18 +26,18 @@ Passing these tests provides more confidence that your implementation is correct
 
 ### Model correctness
 
-These tests compare the model outputs of vLLM against [HF Transformers](https://github.com/huggingface/transformers). You can add new tests under the subdirectories of <gh-dir:tests/models>.
+These tests compare the model outputs of vLLM against [HF Transformers](https://github.com/huggingface/transformers). You can add new tests under the subdirectories of [tests/models](../../../tests/models).
 
 #### Generative models
 
-For [generative models](../../models/generative_models.md), there are two levels of correctness tests, as defined in <gh-file:tests/models/utils.py>:
+For [generative models](../../models/generative_models.md), there are two levels of correctness tests, as defined in [tests/models/utils.py](../../../tests/models/utils.py):
 
 - Exact correctness (`check_outputs_equal`): The text outputted by vLLM should exactly match the text outputted by HF.
 - Logprobs similarity (`check_logprobs_close`): The logprobs outputted by vLLM should be in the top-k logprobs outputted by HF, and vice versa.
 
 #### Pooling models
 
-For [pooling models](../../models/pooling_models.md), we simply check the cosine similarity, as defined in <gh-file:tests/models/utils.py>.
+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 }
 
@@ -45,7 +45,7 @@ For [pooling models](../../models/pooling_models.md), we simply check the cosine
 
 #### Common tests
 
-Adding your model to <gh-file:tests/models/multimodal/processing/test_common.py> verifies that the following input combinations result in the same outputs:
+Adding your model to [tests/models/multimodal/processing/test_common.py](../../../tests/models/multimodal/processing/test_common.py) verifies that the following input combinations result in the same outputs:
 
 - Text + multi-modal data
 - Tokens + multi-modal data
@@ -54,6 +54,6 @@ Adding your model to <gh-file:tests/models/multimodal/processing/test_common.py>
 
 #### Model-specific tests
 
-You can add a new file under <gh-dir:tests/models/multimodal/processing> to run tests that only apply to your model.
+You can add a new file under [tests/models/multimodal/processing](../../../tests/models/multimodal/processing) to run tests that only apply to your model.
 
-For example, if the HF processor for your model accepts user-specified keyword arguments, you can verify that the keyword arguments are being applied correctly, such as in <gh-file:tests/models/multimodal/processing/test_phi3v.py>.
+For example, if the HF processor for your model accepts user-specified keyword arguments, you can verify that the keyword arguments are being applied correctly, such as in [tests/models/multimodal/processing/test_phi3v.py](../../../tests/models/multimodal/processing/test_phi3v.py).

docs/contributing/model/transcription.md

@@ -248,9 +248,9 @@ No extra registration is required beyond having your model class available via t
 
 ## Examples in-tree
 
-- Whisper encoder–decoder (audio-only): <gh-file:vllm/model_executor/models/whisper.py>
-- Voxtral decoder-only (audio embeddings + LLM): <gh-file:vllm/model_executor/models/voxtral.py>
-- Gemma3n decoder-only with fixed instruction prompt: <gh-file:vllm/model_executor/models/gemma3n_mm.py>
+- Whisper encoder–decoder (audio-only): [vllm/model_executor/models/whisper.py](../../../vllm/model_executor/models/whisper.py)
+- Voxtral decoder-only (audio embeddings + LLM): [vllm/model_executor/models/voxtral.py](../../../vllm/model_executor/models/voxtral.py)
+- Gemma3n decoder-only with fixed instruction prompt: [vllm/model_executor/models/gemma3n_mm.py](../../../vllm/model_executor/models/gemma3n_mm.py)
 
 ## Test with the API
 
@@ -278,7 +278,7 @@ Once your model implements `SupportsTranscription`, you can test the endpoints (
       http://localhost:8000/v1/audio/translations
     ```
 
-Or check out more examples in <gh-file:examples/online_serving>.
+Or check out more examples in [examples/online_serving](../../../examples/online_serving).
 
 !!! note
     - If your model handles chunking internally (e.g., via its processor or encoder), set `min_energy_split_window_size=None` in the returned `SpeechToTextConfig` to disable server-side chunking.

docs/contributing/profiling.md

@@ -33,7 +33,7 @@ Traces can be visualized using <https://ui.perfetto.dev/>.
 
 #### Offline Inference
 
-Refer to <gh-file:examples/offline_inference/simple_profiling.py> for an example.
+Refer to [examples/offline_inference/simple_profiling.py](../../examples/offline_inference/simple_profiling.py) for an example.
 
 #### OpenAI Server