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

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

docs/design/fused_moe_modular_kernel.md

@@ -2,7 +2,7 @@
 
 ## Introduction
 
-FusedMoEModularKernel is implemented [here](gh-file:/vllm/model_executor/layers/fused_moe/modular_kernel.py)
+FusedMoEModularKernel is implemented [here](../..//vllm/model_executor/layers/fused_moe/modular_kernel.py)
 
 Based on the format of the input activations, FusedMoE implementations are broadly classified into 2 types.
 
@@ -44,7 +44,7 @@ FusedMoEModularKernel splits the FusedMoE operation into 3 parts,
 
 The TopK Weight Application and Reduction components happen right after the Unpermute operation and before the All2All Combine. Note that the `FusedMoEPermuteExpertsUnpermute` is responsible for the Unpermute and `FusedMoEPrepareAndFinalize` is responsible for the All2All Combine. There is value in doing the TopK Weight Application and Reduction in the `FusedMoEPermuteExpertsUnpermute`. But some implementations choose to do it `FusedMoEPrepareAndFinalize`. In order to enable this flexibility, we have a TopKWeightAndReduce abstract class.
 
-Please find the implementations of TopKWeightAndReduce [here](gh-file:vllm/model_executor/layers/fused_moe/topk_weight_and_reduce.py).
+Please find the implementations of TopKWeightAndReduce [here](../../vllm/model_executor/layers/fused_moe/topk_weight_and_reduce.py).
 
 `FusedMoEPrepareAndFinalize::finalize()` method accepts a `TopKWeightAndReduce` argument that is invoked inside the method.
 The `FusedMoEModularKernel` acts as a bridge between the `FusedMoEPermuteExpertsUnpermute` and `FusedMoEPerpareAndFinalize` implementations to determine where the TopK Weight Application and Reduction happens.
@@ -138,7 +138,7 @@ Typically a FusedMoEPrepareAndFinalize type is backed by an All2All Dispatch & C
 
 #### Step 1: Add an All2All manager
 
-The purpose of the All2All Manager is to set up the All2All kernel implementations. The `FusedMoEPrepareAndFinalize` implementations typically fetch a kernel-implementation "handle" from the All2All Manager to invoke the Dispatch and Combine functions. Please look at the All2All Manager implementations [here](gh-file:vllm/distributed/device_communicators/all2all.py).
+The purpose of the All2All Manager is to set up the All2All kernel implementations. The `FusedMoEPrepareAndFinalize` implementations typically fetch a kernel-implementation "handle" from the All2All Manager to invoke the Dispatch and Combine functions. Please look at the All2All Manager implementations [here](../../vllm/distributed/device_communicators/all2all.py).
 
 #### Step 2: Add a FusedMoEPrepareAndFinalize Type
 
@@ -213,29 +213,29 @@ Please take a look at [init_prepare_finalize](https://github.com/vllm-project/vl
 
 ### How To Unit Test
 
-We have `FusedMoEModularKernel` unit tests at [test_modular_kernel_combinations.py](gh-file:tests/kernels/moe/test_modular_kernel_combinations.py).
+We have `FusedMoEModularKernel` unit tests at [test_modular_kernel_combinations.py](../../tests/kernels/moe/test_modular_kernel_combinations.py).
 
 The unit test iterates through all combinations of `FusedMoEPrepareAndFinalize` and `FusedMoEPremuteExpertsUnpermute` types and if they are
 compatible, runs some correctness tests.
 If you are adding some `FusedMoEPrepareAndFinalize` / `FusedMoEPermuteExpertsUnpermute` implementations,
 
-1. Add the implementation type to `MK_ALL_PREPARE_FINALIZE_TYPES` and `MK_FUSED_EXPERT_TYPES` in [mk_objects.py](gh-file:tests/kernels/moe/modular_kernel_tools/mk_objects.py) respectively.
+1. Add the implementation type to `MK_ALL_PREPARE_FINALIZE_TYPES` and `MK_FUSED_EXPERT_TYPES` in [mk_objects.py](../../tests/kernels/moe/modular_kernel_tools/mk_objects.py) respectively.
 2. Update `Config::is_batched_prepare_finalize()`, `Config::is_batched_fused_experts()`, `Config::is_standard_fused_experts()`,
 `Config::is_fe_16bit_supported()`,  `Config::is_fe_fp8_supported()`, `Config::is_fe_block_fp8_supported()`,
-`Config::is_fe_supports_chunking()` methods in [/tests/kernels/moe/modular_kernel_tools/common.py](gh-file:tests/kernels/moe/modular_kernel_tools/common.py)
+`Config::is_fe_supports_chunking()` methods in [/tests/kernels/moe/modular_kernel_tools/common.py](../../tests/kernels/moe/modular_kernel_tools/common.py)
 
 Doing this will add the new implementation to the test suite.
 
 ### How To Check `FusedMoEPrepareAndFinalize` & `FusedMoEPermuteExpertsUnpermute` Compatibility
 
-The unit test file [test_modular_kernel_combinations.py](gh-file:tests/kernels/moe/test_modular_kernel_combinations.py) can also be executed as a standalone script.
+The unit test file [test_modular_kernel_combinations.py](../../tests/kernels/moe/test_modular_kernel_combinations.py) can also be executed as a standalone script.
 Example: `python3 -m tests.kernels.moe.test_modular_kernel_combinations --pf-type PplxPrepareAndFinalize --experts-type BatchedTritonExperts`
 As a side effect, this script can be used to test `FusedMoEPrepareAndFinalize` & `FusedMoEPermuteExpertsUnpermute` compatibility. When invoked
 with incompatible types, the script will error.
 
 ### How To Profile
 
-Please take a look at [profile_modular_kernel.py](gh-file:tests/kernels/moe/modular_kernel_tools/profile_modular_kernel.py)
+Please take a look at [profile_modular_kernel.py](../../tests/kernels/moe/modular_kernel_tools/profile_modular_kernel.py)
 The script can be used to generate Torch traces for a single `FusedMoEModularKernel::forward()` call for any compatible
 `FusedMoEPrepareAndFinalize` and `FusedMoEPermuteExpertsUnpermute` types.
 Example: `python3 -m tests.kernels.moe.modular_kernel_tools.profile_modular_kernel --pf-type PplxPrepareAndFinalize --experts-type BatchedTritonExperts`

docs/design/io_processor_plugins.md

@@ -6,7 +6,7 @@ When performing an inference with IO Processor plugins, the prompt type is defin
 
 ## Writing an IO Processor Plugin
 
-IO Processor plugins implement the `IOProcessor` interface (<gh-file:vllm/plugins/io_processors/interface.py>):
+IO Processor plugins implement the [`IOProcessor`][vllm.plugins.io_processors.interface.IOProcessor] interface:
 
 ```python
 IOProcessorInput = TypeVar("IOProcessorInput")
@@ -67,9 +67,9 @@ The `parse_request` method is used for validating the user prompt and converting
 The `pre_process*` methods take the validated plugin input to generate vLLM's model prompts for regular inference.
 The `post_process*` methods take `PoolingRequestOutput` objects as input and generate a custom plugin output.
 
-The `output_to_response` method is used only for online serving and converts the plugin output to the `IOProcessorResponse` type that is then returned by the API Server. The implementation of the `/io_processor_pooling` serving endpoint is available here <gh-file:vllm/entrypoints/openai/serving_pooling_with_io_plugin.py>.
+The `output_to_response` method is used only for online serving and converts the plugin output to the `IOProcessorResponse` type that is then returned by the API Server. The implementation of the `/pooling` serving endpoint is available here [vllm/entrypoints/openai/serving_pooling.py](../../vllm/entrypoints/openai/serving_pooling.py).
 
-An example implementation of a plugin that enables generating geotiff images with the PrithviGeospatialMAE model is available [here](https://github.com/christian-pinto/prithvi_io_processor_plugin). Please, also refer to our online (<gh-file:examples/online_serving/prithvi_geospatial_mae.py>) and offline (<gh-file:examples/offline_inference/prithvi_geospatial_mae_io_processor.py>) inference examples.
+An example implementation of a plugin that enables generating geotiff images with the PrithviGeospatialMAE model is available [here](https://github.com/christian-pinto/prithvi_io_processor_plugin). Please, also refer to our online ([examples/online_serving/prithvi_geospatial_mae.py](../../examples/online_serving/prithvi_geospatial_mae.py)) and offline ([examples/offline_inference/prithvi_geospatial_mae_io_processor.py](../../examples/offline_inference/prithvi_geospatial_mae_io_processor.py)) inference examples.
 
 ## Using an IO Processor plugin
 

docs/design/metrics.md

@@ -80,13 +80,13 @@ The subset of metrics exposed in the Grafana dashboard gives us an indication of
 - `vllm:request_decode_time_seconds` - Requests decode time.
 - `vllm:request_max_num_generation_tokens` - Max generation tokens in a sequence group.
 
-See [the PR which added this Dashboard](gh-pr:2316) for interesting and useful background on the choices made here.
+See [the PR which added this Dashboard](https://github.com/vllm-project/vllm/pull/2316) for interesting and useful background on the choices made here.
 
 ### Prometheus Client Library
 
-Prometheus support was initially added [using the aioprometheus library](gh-pr:1890), but a switch was made quickly to [prometheus_client](gh-pr:2730). The rationale is discussed in both linked PRs.
+Prometheus support was initially added [using the aioprometheus library](https://github.com/vllm-project/vllm/pull/1890), but a switch was made quickly to [prometheus_client](https://github.com/vllm-project/vllm/pull/2730). The rationale is discussed in both linked PRs.
 
-With the switch to `aioprometheus`, we lost a `MetricsMiddleware` to track HTTP metrics, but this was reinstated [using prometheus_fastapi_instrumentator](gh-pr:15657):
+With the switch to `aioprometheus`, we lost a `MetricsMiddleware` to track HTTP metrics, but this was reinstated [using prometheus_fastapi_instrumentator](https://github.com/vllm-project/vllm/pull/15657):
 
 ```bash
 $ curl http://0.0.0.0:8000/metrics 2>/dev/null  | grep -P '^http_(?!.*(_bucket|_created|_sum)).*'
@@ -99,7 +99,7 @@ http_request_duration_seconds_count{handler="/v1/completions",method="POST"} 201
 
 ### Multi-process Mode
 
-In v0, metrics are collected in the engine core process and we use multiprocess mode to make them available in the API server process. See <gh-pr:7279>.
+In v0, metrics are collected in the engine core process and we use multiprocess mode to make them available in the API server process. See <https://github.com/vllm-project/vllm/pull/7279>.
 
 ### Built in Python/Process Metrics
 
@@ -125,32 +125,32 @@ vLLM instance.
 
 For background, these are some of the relevant PRs which added the v0 metrics:
 
-- <gh-pr:1890>
-- <gh-pr:2316>
-- <gh-pr:2730>
-- <gh-pr:4464>
-- <gh-pr:7279>
+- <https://github.com/vllm-project/vllm/pull/1890>
+- <https://github.com/vllm-project/vllm/pull/2316>
+- <https://github.com/vllm-project/vllm/pull/2730>
+- <https://github.com/vllm-project/vllm/pull/4464>
+- <https://github.com/vllm-project/vllm/pull/7279>
 
-Also note the ["Even Better Observability"](gh-issue:3616) feature where e.g. [a detailed roadmap was laid out](gh-issue:3616#issuecomment-2030858781).
+Also note the ["Even Better Observability"](https://github.com/vllm-project/vllm/issues/3616) feature where e.g. [a detailed roadmap was laid out](https://github.com/vllm-project/vllm/issues/3616#issuecomment-2030858781).
 
 ## v1 Design
 
 ### v1 PRs
 
 For background, here are the relevant v1 PRs relating to the v1
-metrics issue <gh-issue:10582>:
-
-- <gh-pr:11962>
-- <gh-pr:11973>
-- <gh-pr:10907>
-- <gh-pr:12416>
-- <gh-pr:12478>
-- <gh-pr:12516>
-- <gh-pr:12530>
-- <gh-pr:12561>
-- <gh-pr:12579>
-- <gh-pr:12592>
-- <gh-pr:12644>
+metrics issue <https://github.com/vllm-project/vllm/issues/10582>:
+
+- <https://github.com/vllm-project/vllm/pull/11962>
+- <https://github.com/vllm-project/vllm/pull/11973>
+- <https://github.com/vllm-project/vllm/pull/10907>
+- <https://github.com/vllm-project/vllm/pull/12416>
+- <https://github.com/vllm-project/vllm/pull/12478>
+- <https://github.com/vllm-project/vllm/pull/12516>
+- <https://github.com/vllm-project/vllm/pull/12530>
+- <https://github.com/vllm-project/vllm/pull/12561>
+- <https://github.com/vllm-project/vllm/pull/12579>
+- <https://github.com/vllm-project/vllm/pull/12592>
+- <https://github.com/vllm-project/vllm/pull/12644>
 
 ### Metrics Collection
 
@@ -369,7 +369,7 @@ vllm:cache_config_info{block_size="16",cache_dtype="auto",calculate_kv_scales="F
 
 However, `prometheus_client` has
 [never supported Info metrics in multiprocessing mode](https://github.com/prometheus/client_python/pull/300) -
-for [unclear reasons](gh-pr:7279#discussion_r1710417152). We
+for [unclear reasons](https://github.com/vllm-project/vllm/pull/7279#discussion_r1710417152). We
 simply use a `Gauge` metric set to 1 and
 `multiprocess_mode="mostrecent"` instead.
 
@@ -394,7 +394,7 @@ distinguish between per-adapter counts. This should be revisited.
 Note that `multiprocess_mode="livemostrecent"` is used - the most
 recent metric is used, but only from currently running processes.
 
-This was added in <gh-pr:9477> and there is
+This was added in <https://github.com/vllm-project/vllm/pull/9477> and there is
 [at least one known user](https://github.com/kubernetes-sigs/gateway-api-inference-extension/pull/54).
 If we revisit this design and deprecate the old metric, we should reduce
 the need for a significant deprecation period by making the change in
@@ -402,7 +402,7 @@ v0 also and asking this project to move to the new metric.
 
 ### Prefix Cache metrics
 
-The discussion in <gh-issue:10582> about adding prefix cache metrics yielded
+The discussion in <https://github.com/vllm-project/vllm/issues/10582> about adding prefix cache metrics yielded
 some interesting points which may be relevant to how we approach
 future metrics.
 
@@ -439,8 +439,8 @@ suddenly (from their perspective) when it is removed, even if there is
 an equivalent metric for them to use.
 
 As an example, see how `vllm:avg_prompt_throughput_toks_per_s` was
-[deprecated](gh-pr:2764) (with a comment in the code),
-[removed](gh-pr:12383), and then [noticed by a user](gh-issue:13218).
+[deprecated](https://github.com/vllm-project/vllm/pull/2764) (with a comment in the code),
+[removed](https://github.com/vllm-project/vllm/pull/12383), and then [noticed by a user](https://github.com/vllm-project/vllm/issues/13218).
 
 In general:
 
@@ -460,20 +460,20 @@ the project-wide deprecation policy.
 
 ### Unimplemented - `vllm:tokens_total`
 
-Added by <gh-pr:4464>, but apparently never implemented. This can just be
+Added by <https://github.com/vllm-project/vllm/pull/4464>, but apparently never implemented. This can just be
 removed.
 
 ### Duplicated - Queue Time
 
 The `vllm:time_in_queue_requests` Histogram metric was added by
-<gh-pr:9659> and its calculation is:
+<https://github.com/vllm-project/vllm/pull/9659> and its calculation is:
 
 ```python
     self.metrics.first_scheduled_time = now
     self.metrics.time_in_queue = now - self.metrics.arrival_time
 ```
 
-Two weeks later, <gh-pr:4464> added `vllm:request_queue_time_seconds` leaving
+Two weeks later, <https://github.com/vllm-project/vllm/pull/4464> added `vllm:request_queue_time_seconds` leaving
 us with:
 
 ```python
@@ -513,7 +513,7 @@ cache to complete other requests), we swap kv cache blocks out to CPU
 memory. This is also known as "KV cache offloading" and is configured
 with `--swap-space` and `--preemption-mode`.
 
-In v0, [vLLM has long supported beam search](gh-issue:6226). The
+In v0, [vLLM has long supported beam search](https://github.com/vllm-project/vllm/issues/6226). The
 SequenceGroup encapsulated the idea of N Sequences which
 all shared the same prompt kv blocks. This enabled KV cache block
 sharing between requests, and copy-on-write to do branching. CPU
@@ -526,7 +526,7 @@ and the part of the prompt that was evicted can be recomputed.
 
 SequenceGroup was removed in V1, although a replacement will be
 required for "parallel sampling" (`n>1`).
-[Beam search was moved out of the core (in V0)](gh-issue:8306). There was a
+[Beam search was moved out of the core (in V0)](https://github.com/vllm-project/vllm/issues/8306). There was a
 lot of complex code for a very uncommon feature.
 
 In V1, with prefix caching being better (zero over head) and therefore
@@ -541,7 +541,7 @@ Some v0 metrics are only relevant in the context of "parallel
 sampling". This is where the `n` parameter in a request is used to
 request multiple completions from the same prompt.
 
-As part of adding parallel sampling support in <gh-pr:10980>, we should
+As part of adding parallel sampling support in <https://github.com/vllm-project/vllm/pull/10980>, we should
 also add these metrics.
 
 - `vllm:request_params_n` (Histogram)
@@ -566,7 +566,7 @@ model and then validate those tokens with the larger model.
 - `vllm:spec_decode_num_draft_tokens_total` (Counter)
 - `vllm:spec_decode_num_emitted_tokens_total` (Counter)
 
-There is a PR under review (<gh-pr:12193>) to add "prompt lookup (ngram)"
+There is a PR under review (<https://github.com/vllm-project/vllm/pull/12193>) to add "prompt lookup (ngram)"
 speculative decoding to v1. Other techniques will follow. We should
 revisit the v0 metrics in this context.
 
@@ -587,7 +587,7 @@ see:
 - [Standardizing Large Model Server Metrics in Kubernetes](https://docs.google.com/document/d/1SpSp1E6moa4HSrJnS4x3NpLuj88sMXr2tbofKlzTZpk)
 - [Benchmarking LLM Workloads for Performance Evaluation and Autoscaling in Kubernetes](https://docs.google.com/document/d/1k4Q4X14hW4vftElIuYGDu5KDe2LtV1XammoG-Xi3bbQ)
 - [Inference Perf](https://github.com/kubernetes-sigs/wg-serving/tree/main/proposals/013-inference-perf)
-- <gh-issue:5041> and <gh-pr:12726>.
+- <https://github.com/vllm-project/vllm/issues/5041> and <https://github.com/vllm-project/vllm/pull/12726>.
   
 This is a non-trivial topic. Consider this comment from Rob:
 
@@ -654,7 +654,7 @@ fall under the more general heading of "Observability".
 
 v0 has support for OpenTelemetry tracing:
 
-- Added by <gh-pr:4687>
+- Added by <https://github.com/vllm-project/vllm/pull/4687>
 - Configured with `--oltp-traces-endpoint` and `--collect-detailed-traces`
 - [OpenTelemetry blog post](https://opentelemetry.io/blog/2024/llm-observability/)
 - [User-facing docs](../examples/online_serving/opentelemetry.md)
@@ -685,7 +685,7 @@ documentation for this option states:
 > use of possibly costly and or blocking operations and hence might
 > have a performance impact.
 
-The metrics were added by <gh-pr:7089> and who up in an OpenTelemetry trace
+The metrics were added by <https://github.com/vllm-project/vllm/pull/7089> and who up in an OpenTelemetry trace
 as:
 
 ```text

docs/design/mm_processing.md

@@ -60,7 +60,7 @@ With the help of dummy text and automatic prompt updating, our multi-modal proce
 
 ## Processor Output Caching
 
-Some HF processors, such as the one for Qwen2-VL, are [very slow](gh-issue:9238). To alleviate this problem, we cache the multi-modal outputs of HF processor to avoid processing the same multi-modal input (e.g. image) again.
+Some HF processors, such as the one for Qwen2-VL, are [very slow](https://github.com/vllm-project/vllm/issues/9238). To alleviate this problem, we cache the multi-modal outputs of HF processor to avoid processing the same multi-modal input (e.g. image) again.
 
 When new data is passed in, we first check which items are in the cache, and which ones are missing. The missing items are passed into the HF processor in a single batch and cached, before being merged with the existing items in the cache.
 

docs/design/multiprocessing.md

@@ -82,7 +82,7 @@ There are other miscellaneous places hard-coding the use of `spawn`:
 
 Related PRs:
 
-- <gh-pr:8823>
+- <https://github.com/vllm-project/vllm/pull/8823>
 
 ## Prior State in v1
 

docs/design/torch_compile.md

@@ -19,8 +19,8 @@ vLLM will take all the available factors into consideration, and decide a direct
 
 The factors considered include:
 
-- All the related configs (see the `compute_hash` functions in their respective configs in the [config folder](gh-file:vllm/config))
-- PyTorch configs (see the `compute_hash` functions in the [compiler_interface.py](gh-file:vllm/compilation/compiler_interface.py))
+- All the related configs (see the `compute_hash` functions in their respective configs in the [config folder](../../vllm/config))
+- PyTorch configs (see the `compute_hash` functions in the [compiler_interface.py](../../vllm/compilation/compiler_interface.py))
 - The model's forward function and the relevant functions called by the forward function (see below)
 
 With all these factors taken into consideration, usually we can guarantee that the cache is safe to use, and will not cause any unexpected behavior. Therefore, the cache is enabled by default. If you want to debug the compilation process, or if you suspect the cache is causing some issues, you can disable it by setting the environment variable `VLLM_DISABLE_COMPILE_CACHE=1`.

docs/features/README.md

@@ -44,15 +44,15 @@ th:not(:first-child) {
 | [SD](spec_decode.md) | ✅ | ✅ | ❌ | ✅ | | | | | | | | | | | |
 | CUDA graph | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | | |
 | [pooling](../models/pooling_models.md) | 🟠\* | 🟠\* | ✅ | ❌ | ✅ | ✅ | | | | | | | | | |
-| <abbr title="Encoder-Decoder Models">enc-dec</abbr> | ❌ | [❌](gh-issue:7366) | ❌ | [❌](gh-issue:7366) | ✅ | ✅ | ✅ | | | | | | | | |
+| <abbr title="Encoder-Decoder Models">enc-dec</abbr> | ❌ | [❌](https://github.com/vllm-project/vllm/issues/7366) | ❌ | [❌](https://github.com/vllm-project/vllm/issues/7366) | ✅ | ✅ | ✅ | | | | | | | | |
 | <abbr title="Logprobs">logP</abbr> | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | | | | | | | |
 | <abbr title="Prompt Logprobs">prmpt logP</abbr> | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | | | | | | |
 | <abbr title="Async Output Processing">async output</abbr> | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | | | | | |
 | multi-step | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | | | | |
-| [mm](multimodal_inputs.md) | ✅ | ✅ | [🟠](gh-pr:4194)<sup>^</sup> | ❔ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❔ | ✅ | | | |
-| best-of | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ✅ | ✅ | | |
-| beam-search | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ❔ | ✅ | ✅ | |
-| [prompt-embeds](prompt_embeds.md) | ✅ | [❌](gh-issue:25096) | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❔ | ❔ | ❌ | ❔ | ❔ | ✅ |
+| [mm](multimodal_inputs.md) | ✅ | ✅ | [🟠](https://github.com/vllm-project/vllm/pull/4194)<sup>^</sup> | ❔ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❔ | ✅ | | | |
+| best-of | ✅ | ✅ | ✅ | [❌](https://github.com/vllm-project/vllm/issues/6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](https://github.com/vllm-project/vllm/issues/7968) | ✅ | ✅ | | |
+| beam-search | ✅ | ✅ | ✅ | [❌](https://github.com/vllm-project/vllm/issues/6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](https://github.com/vllm-project/vllm/issues/7968) | ❔ | ✅ | ✅ | |
+| [prompt-embeds](prompt_embeds.md) | ✅ | [❌](https://github.com/vllm-project/vllm/issues/25096) | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❔ | ❔ | ❌ | ❔ | ❔ | ✅ |
 
 \* Chunked prefill and prefix caching are only applicable to last-token pooling.  
 <sup>^</sup> LoRA is only applicable to the language backbone of multimodal models.
@@ -63,18 +63,18 @@ th:not(:first-child) {
 
 | Feature                                                   | Volta               | Turing    | Ampere    | Ada    | Hopper     | CPU                | AMD    | TPU | Intel GPU |
 |-----------------------------------------------------------|---------------------|-----------|-----------|--------|------------|--------------------|--------|-----| ------------|
-| [CP][chunked-prefill]                                     | [❌](gh-issue:2729) | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ | ✅        |
-| [APC](automatic_prefix_caching.md)                        | [❌](gh-issue:3687) | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ | ✅        |
+| [CP][chunked-prefill]                                     | [❌](https://github.com/vllm-project/vllm/issues/2729) | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ | ✅        |
+| [APC](automatic_prefix_caching.md)                        | [❌](https://github.com/vllm-project/vllm/issues/3687) | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ | ✅        |
 | [LoRA](lora.md)                                           | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ✅ | ✅        |
-| [SD](spec_decode.md)                                      | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ | [🟠](gh-issue:26963)       |
-| CUDA graph                                                | ✅                  | ✅        | ✅        | ✅     | ✅        | ❌                  | ✅     | ❌ | [❌](gh-issue:26970)        |
+| [SD](spec_decode.md)                                      | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ | [🟠](https://github.com/vllm-project/vllm/issues/26963)       |
+| CUDA graph                                                | ✅                  | ✅        | ✅        | ✅     | ✅        | ❌                  | ✅     | ❌ | [❌](https://github.com/vllm-project/vllm/issues/26970)        |
 | [pooling](../models/pooling_models.md)                    | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ | ✅        |
 | <abbr title="Encoder-Decoder Models">enc-dec</abbr>       | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ❌     | ❌ | ✅        |
-| [mm](multimodal_inputs.md)                                | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ | [🟠](gh-issue:26965)       |
+| [mm](multimodal_inputs.md)                                | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ | [🟠](https://github.com/vllm-project/vllm/issues/26965)       |
 | <abbr title="Logprobs">logP</abbr>                        | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ | ✅        |
 | <abbr title="Prompt Logprobs">prmpt logP</abbr>           | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ | ✅        |
 | <abbr title="Async Output Processing">async output</abbr> | ✅                  | ✅        | ✅        | ✅     | ✅        | ❌                  | ❌     | ❌ | ✅        |
-| multi-step                                                | ✅                  | ✅        | ✅        | ✅     | ✅        | [❌](gh-issue:8477) | ✅     | ❌ | ✅        |
+| multi-step                                                | ✅                  | ✅        | ✅        | ✅     | ✅        | [❌](https://github.com/vllm-project/vllm/issues/8477) | ✅     | ❌ | ✅        |
 | best-of                                                   | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ | ✅        |
 | beam-search                                               | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ | ✅        |
-| [prompt-embeds](prompt_embeds.md)                         | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ?     | [❌](gh-issue:25097) | ✅       |
+| [prompt-embeds](prompt_embeds.md)                         | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ?     | [❌](https://github.com/vllm-project/vllm/issues/25097) | ✅       |

docs/features/automatic_prefix_caching.md

@@ -11,7 +11,7 @@ Automatic Prefix Caching (APC in short) caches the KV cache of existing queries,
 
 Set `enable_prefix_caching=True` in vLLM engine to enable APC. Here is an example:
 
-<gh-file:examples/offline_inference/automatic_prefix_caching.py>
+[examples/offline_inference/automatic_prefix_caching.py](../../examples/offline_inference/automatic_prefix_caching.py)
 
 ## Example workloads
 

docs/features/disagg_prefill.md

@@ -17,14 +17,14 @@ Two main reasons:
 
 ## Usage example
 
-Please refer to <gh-file:examples/online_serving/disaggregated_prefill.sh> for the example usage of disaggregated prefilling.
+Please refer to [examples/online_serving/disaggregated_prefill.sh](../../examples/online_serving/disaggregated_prefill.sh) for the example usage of disaggregated prefilling.
 
 Now supports 5 types of connectors:
 
-- **SharedStorageConnector**: refer to <gh-file:examples/offline_inference/disaggregated-prefill-v1/run.sh> for the example usage of SharedStorageConnector disaggregated prefilling.
-- **LMCacheConnectorV1**: refer to <gh-file:examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh> for the example usage of LMCacheConnectorV1 disaggregated prefilling which uses NIXL as the underlying KV transmission.
-- **NixlConnector**: refer to <gh-file:tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh> for the example usage of NixlConnector disaggregated prefilling which support fully async send/recv. For detailed usage guide, see [NixlConnector Usage Guide](nixl_connector_usage.md).
-- **P2pNcclConnector**: refer to <gh-file:examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh> for the example usage of P2pNcclConnector disaggregated prefilling.
+- **SharedStorageConnector**: refer to [examples/offline_inference/disaggregated-prefill-v1/run.sh](../../examples/offline_inference/disaggregated-prefill-v1/run.sh) for the example usage of SharedStorageConnector disaggregated prefilling.
+- **LMCacheConnectorV1**: refer to [examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh](../../examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh) for the example usage of LMCacheConnectorV1 disaggregated prefilling which uses NIXL as the underlying KV transmission.
+- **NixlConnector**: refer to [tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh](../../tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh) for the example usage of NixlConnector disaggregated prefilling which support fully async send/recv. For detailed usage guide, see [NixlConnector Usage Guide](nixl_connector_usage.md).
+- **P2pNcclConnector**: refer to [examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh](../../examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh) for the example usage of P2pNcclConnector disaggregated prefilling.
 - **MultiConnector**: take advantage of the kv_connector_extra_config: dict[str, Any] already present in KVTransferConfig to stash all the connectors we want in an ordered list of kwargs.such as:
 
   ```bash
@@ -45,7 +45,7 @@ For NixlConnector, you may also specify one or multiple NIXL_Backend. Such as:
 
 ## Benchmarks
 
-Please refer to <gh-file:benchmarks/disagg_benchmarks> for disaggregated prefilling benchmarks.
+Please refer to [benchmarks/disagg_benchmarks](../../benchmarks/disagg_benchmarks) for disaggregated prefilling benchmarks.
 
 ## Development
 

docs/features/lora.md

@@ -47,7 +47,7 @@ the third parameter is the path to the LoRA adapter.
     )
     ```
 
-Check out <gh-file:examples/offline_inference/multilora_inference.py> for an example of how to use LoRA adapters with the async engine and how to use more advanced configuration options.
+Check out [examples/offline_inference/multilora_inference.py](../../examples/offline_inference/multilora_inference.py) for an example of how to use LoRA adapters with the async engine and how to use more advanced configuration options.
 
 ## Serving LoRA Adapters
 

docs/features/multimodal_inputs.md

@@ -3,7 +3,7 @@
 This page teaches you how to pass multi-modal inputs to [multi-modal models][supported-mm-models] in vLLM.
 
 !!! note
-    We are actively iterating on multi-modal support. See [this RFC](gh-issue:4194) for upcoming changes,
+    We are actively iterating on multi-modal support. See [this RFC](https://github.com/vllm-project/vllm/issues/4194) for upcoming changes,
     and [open an issue on GitHub](https://github.com/vllm-project/vllm/issues/new/choose) if you have any feedback or feature requests.
 
 !!! tip
@@ -129,7 +129,7 @@ You can pass a single image to the `'image'` field of the multi-modal dictionary
         print(generated_text)
     ```
 
-Full example: <gh-file:examples/offline_inference/vision_language.py>
+Full example: [examples/offline_inference/vision_language.py](../../examples/offline_inference/vision_language.py)
 
 To substitute multiple images inside the same text prompt, you can pass in a list of images instead:
 
@@ -162,7 +162,7 @@ To substitute multiple images inside the same text prompt, you can pass in a lis
         print(generated_text)
     ```
 
-Full example: <gh-file:examples/offline_inference/vision_language_multi_image.py>
+Full example: [examples/offline_inference/vision_language_multi_image.py](../../examples/offline_inference/vision_language_multi_image.py)
 
 If using the [LLM.chat](../models/generative_models.md#llmchat) method, you can pass images directly in the message content using various formats: image URLs, PIL Image objects, or pre-computed embeddings:
 
@@ -346,13 +346,13 @@ Instead of NumPy arrays, you can also pass `'torch.Tensor'` instances, as shown
     !!! note
         'process_vision_info' is only applicable to Qwen2.5-VL and similar models.
 
-Full example: <gh-file:examples/offline_inference/vision_language.py>
+Full example: [examples/offline_inference/vision_language.py](../../examples/offline_inference/vision_language.py)
 
 ### Audio Inputs
 
 You can pass a tuple `(array, sampling_rate)` to the `'audio'` field of the multi-modal dictionary.
 
-Full example: <gh-file:examples/offline_inference/audio_language.py>
+Full example: [examples/offline_inference/audio_language.py](../../examples/offline_inference/audio_language.py)
 
 ### Embedding Inputs
 
@@ -434,11 +434,11 @@ Our OpenAI-compatible server accepts multi-modal data via the [Chat Completions
     A chat template is **required** to use Chat Completions API.
     For HF format models, the default chat template is defined inside `chat_template.json` or `tokenizer_config.json`.
 
-    If no default chat template is available, we will first look for a built-in fallback in <gh-file:vllm/transformers_utils/chat_templates/registry.py>.
+    If no default chat template is available, we will first look for a built-in fallback in [vllm/transformers_utils/chat_templates/registry.py](../../vllm/transformers_utils/chat_templates/registry.py).
     If no fallback is available, an error is raised and you have to provide the chat template manually via the `--chat-template` argument.
 
-    For certain models, we provide alternative chat templates inside <gh-dir:examples>.
-    For example, VLM2Vec uses <gh-file:examples/template_vlm2vec_phi3v.jinja> which is different from the default one for Phi-3-Vision.
+    For certain models, we provide alternative chat templates inside [examples](../../examples).
+    For example, VLM2Vec uses [examples/template_vlm2vec_phi3v.jinja](../../examples/template_vlm2vec_phi3v.jinja) which is different from the default one for Phi-3-Vision.
 
 ### Image Inputs
 
@@ -524,7 +524,7 @@ Then, you can use the OpenAI client as follows:
     print("Chat completion output:", chat_response.choices[0].message.content)
     ```
 
-Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for_multimodal.py>
+Full example: [examples/online_serving/openai_chat_completion_client_for_multimodal.py](../../examples/online_serving/openai_chat_completion_client_for_multimodal.py)
 
 !!! tip
     Loading from local file paths is also supported on vLLM: You can specify the allowed local media path via `--allowed-local-media-path` when launching the API server/engine,
@@ -595,7 +595,7 @@ Then, you can use the OpenAI client as follows:
     print("Chat completion output from image url:", result)
     ```
 
-Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for_multimodal.py>
+Full example: [examples/online_serving/openai_chat_completion_client_for_multimodal.py](../../examples/online_serving/openai_chat_completion_client_for_multimodal.py)
 
 !!! note
     By default, the timeout for fetching videos through HTTP URL is `30` seconds.
@@ -719,7 +719,7 @@ Alternatively, you can pass `audio_url`, which is the audio counterpart of `imag
     print("Chat completion output from audio url:", result)
     ```
 
-Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for_multimodal.py>
+Full example: [examples/online_serving/openai_chat_completion_client_for_multimodal.py](../../examples/online_serving/openai_chat_completion_client_for_multimodal.py)
 
 !!! note
     By default, the timeout for fetching audios through HTTP URL is `10` seconds.

docs/features/nixl_connector_usage.md

@@ -9,7 +9,7 @@ NixlConnector is a high-performance KV cache transfer connector for vLLM's disag
 Install the NIXL library: `uv pip install nixl`, as a quick start.
 
 - Refer to [NIXL official repository](https://github.com/ai-dynamo/nixl) for more installation instructions
-- The specified required NIXL version can be found in [requirements/kv_connectors.txt](gh-file:requirements/kv_connectors.txt) and other relevant config files
+- The specified required NIXL version can be found in [requirements/kv_connectors.txt](../../requirements/kv_connectors.txt) and other relevant config files
 
 For non-cuda platform, please install nixl with ucx build from source, instructed as below.
 
@@ -170,6 +170,6 @@ Support use case: Prefill with 'HND' and decode with 'NHD' with experimental con
 
 Refer to these example scripts in the vLLM repository:
 
-- [run_accuracy_test.sh](gh-file:tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh)
-- [toy_proxy_server.py](gh-file:tests/v1/kv_connector/nixl_integration/toy_proxy_server.py)
-- [test_accuracy.py](gh-file:tests/v1/kv_connector/nixl_integration/test_accuracy.py)
+- [run_accuracy_test.sh](../../tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh)
+- [toy_proxy_server.py](../../tests/v1/kv_connector/nixl_integration/toy_proxy_server.py)
+- [test_accuracy.py](../../tests/v1/kv_connector/nixl_integration/test_accuracy.py)

docs/features/prompt_embeds.md

@@ -16,7 +16,7 @@ To input multi-modal data, follow this schema in [vllm.inputs.EmbedsPrompt][]:
 
 You can pass prompt embeddings from Hugging Face Transformers models to the  `'prompt_embeds'` field of the prompt embedding dictionary, as shown in the following examples:
 
-<gh-file:examples/offline_inference/prompt_embed_inference.py>
+[examples/offline_inference/prompt_embed_inference.py](../../examples/offline_inference/prompt_embed_inference.py)
 
 ## Online Serving
 
@@ -37,4 +37,4 @@ vllm serve meta-llama/Llama-3.2-1B-Instruct --runner generate \
 
 Then, you can use the OpenAI client as follows:
 
-<gh-file:examples/online_serving/prompt_embed_inference_with_openai_client.py>
+[examples/online_serving/prompt_embed_inference_with_openai_client.py](../../examples/online_serving/prompt_embed_inference_with_openai_client.py)

docs/features/quantization/README.md

@@ -64,4 +64,4 @@ th:not(:first-child) {
 !!! note
     This compatibility chart is subject to change as vLLM continues to evolve and expand its support for different hardware platforms and quantization methods.
 
-    For the most up-to-date information on hardware support and quantization methods, please refer to <gh-dir:vllm/model_executor/layers/quantization> or consult with the vLLM development team.
+    For the most up-to-date information on hardware support and quantization methods, please refer to [vllm/model_executor/layers/quantization](../../../vllm/model_executor/layers/quantization) or consult with the vLLM development team.

docs/features/reasoning_outputs.md

@@ -196,7 +196,7 @@ The reasoning content is also available when both tool calling and the reasoning
     print(f"Arguments: {tool_call.arguments}")
     ```
 
-For more examples, please refer to <gh-file:examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py>.
+For more examples, please refer to [examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py](../../examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py).
 
 ## Limitations
 
@@ -204,7 +204,7 @@ For more examples, please refer to <gh-file:examples/online_serving/openai_chat_
 
 ## How to support a new reasoning model
 
-You can add a new `ReasoningParser` similar to <gh-file:vllm/reasoning/deepseek_r1_reasoning_parser.py>.
+You can add a new `ReasoningParser` similar to [vllm/reasoning/deepseek_r1_reasoning_parser.py](../../vllm/reasoning/deepseek_r1_reasoning_parser.py).
 
 ??? code
 
@@ -264,7 +264,7 @@ You can add a new `ReasoningParser` similar to <gh-file:vllm/reasoning/deepseek_
             """
     ```
 
-Additionally, to enable structured output, you'll need to create a new `Reasoner` similar to the one in <gh-file:vllm/reasoning/deepseek_r1_reasoning_parser.py>.
+Additionally, to enable structured output, you'll need to create a new `Reasoner` similar to the one in [vllm/reasoning/deepseek_r1_reasoning_parser.py](../../vllm/reasoning/deepseek_r1_reasoning_parser.py).
 
 ??? code
 

docs/features/spec_decode.md

@@ -3,7 +3,7 @@
 !!! warning
     Please note that speculative decoding in vLLM is not yet optimized and does
     not usually yield inter-token latency reductions for all prompt datasets or sampling parameters.
-    The work to optimize it is ongoing and can be followed here: <gh-issue:4630>
+    The work to optimize it is ongoing and can be followed here: <https://github.com/vllm-project/vllm/issues/4630>
 
 !!! warning
     Currently, speculative decoding in vLLM is not compatible with pipeline parallelism.
@@ -183,7 +183,7 @@ A variety of speculative models of this type are available on HF hub:
 ## Speculating using EAGLE based draft models
 
 The following code configures vLLM to use speculative decoding where proposals are generated by
-an [EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency)](https://arxiv.org/pdf/2401.15077) based draft model. A more detailed example for offline mode, including how to extract request level acceptance rate, can be found [here](gh-file:examples/offline_inference/eagle.py).
+an [EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency)](https://arxiv.org/pdf/2401.15077) based draft model. A more detailed example for offline mode, including how to extract request level acceptance rate, can be found [here](../../examples/offline_inference/spec_decode.py).
 
 ??? code
 
@@ -218,8 +218,8 @@ an [EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency)](https
 A few important things to consider when using the EAGLE based draft models:
 
 1. The EAGLE draft models available in the [HF repository for EAGLE models](https://huggingface.co/yuhuili) should
-   be able to be loaded and used directly by vLLM after <gh-pr:12304>.
-   If you are using vllm version before <gh-pr:12304>, please use the
+   be able to be loaded and used directly by vLLM after <https://github.com/vllm-project/vllm/pull/12304>.
+   If you are using vllm version before <https://github.com/vllm-project/vllm/pull/12304>, please use the
    [script](https://gist.github.com/abhigoyal1997/1e7a4109ccb7704fbc67f625e86b2d6d) to convert the speculative model,
    and specify `"model": "path/to/modified/eagle/model"` in `speculative_config`. If weight-loading problems still occur when using the latest version of vLLM, please leave a comment or raise an issue.
 
@@ -229,7 +229,7 @@ A few important things to consider when using the EAGLE based draft models:
 
 3. When using EAGLE-based speculators with vLLM, the observed speedup is lower than what is
    reported in the reference implementation [here](https://github.com/SafeAILab/EAGLE). This issue is under
-   investigation and tracked here: <gh-issue:9565>.
+   investigation and tracked here: <https://github.com/vllm-project/vllm/issues/9565>.
 
 4. When using EAGLE-3 based draft model, option "method" must be set to "eagle3".
    That is, to specify `"method": "eagle3"` in `speculative_config`.
@@ -267,7 +267,7 @@ speculative decoding, breaking down the guarantees into three key areas:
     >   distribution. [View Test Code](https://github.com/vllm-project/vllm/blob/47b65a550866c7ffbd076ecb74106714838ce7da/tests/samplers/test_rejection_sampler.py#L252)
     > - **Greedy Sampling Equality**: Confirms that greedy sampling with speculative decoding matches greedy sampling
     >   without it. This verifies that vLLM's speculative decoding framework, when integrated with the vLLM forward pass and the vLLM rejection sampler,
-    >   provides a lossless guarantee. Almost all of the tests in <gh-dir:tests/spec_decode/e2e>.
+    >   provides a lossless guarantee. Almost all of the tests in [tests/spec_decode/e2e](../../tests/spec_decode/e2e).
     >   verify this property using [this assertion implementation](https://github.com/vllm-project/vllm/blob/b67ae00cdbbe1a58ffc8ff170f0c8d79044a684a/tests/spec_decode/e2e/conftest.py#L291)
 
 3. **vLLM Logprob Stability**
@@ -289,4 +289,4 @@ For mitigation strategies, please refer to the FAQ entry *Can the output of a pr
 - [A Hacker's Guide to Speculative Decoding in vLLM](https://www.youtube.com/watch?v=9wNAgpX6z_4)
 - [What is Lookahead Scheduling in vLLM?](https://docs.google.com/document/d/1Z9TvqzzBPnh5WHcRwjvK2UEeFeq5zMZb5mFE8jR0HCs/edit#heading=h.1fjfb0donq5a)
 - [Information on batch expansion](https://docs.google.com/document/d/1T-JaS2T1NRfdP51qzqpyakoCXxSXTtORppiwaj5asxA/edit#heading=h.kk7dq05lc6q8)
-- [Dynamic speculative decoding](gh-issue:4565)
+- [Dynamic speculative decoding](https://github.com/vllm-project/vllm/issues/4565)

docs/features/structured_outputs.md

@@ -298,7 +298,7 @@ Step #2: explanation="Next, let's isolate 'x' by dividing both sides of the equa
 Answer: x = -29/8
 ```
 
-An example of using `structural_tag` can be found here: <gh-file:examples/online_serving/structured_outputs>
+An example of using `structural_tag` can be found here: [examples/online_serving/structured_outputs](../../examples/online_serving/structured_outputs)
 
 ## Offline Inference
 

docs/features/tool_calling.md

@@ -151,9 +151,9 @@ Known issues:
    much shorter than what vLLM generates. Since an exception is thrown when this condition
    is not met, the following additional chat templates are provided:
 
-    * <gh-file:examples/tool_chat_template_mistral.jinja> - this is the "official" Mistral chat template, but tweaked so that
+    * [examples/tool_chat_template_mistral.jinja](../../examples/tool_chat_template_mistral.jinja) - this is the "official" Mistral chat template, but tweaked so that
       it works with vLLM's tool call IDs (provided `tool_call_id` fields are truncated to the last 9 digits)
-    * <gh-file:examples/tool_chat_template_mistral_parallel.jinja> - this is a "better" version that adds a tool-use system prompt
+    * [examples/tool_chat_template_mistral_parallel.jinja](../../examples/tool_chat_template_mistral_parallel.jinja) - this is a "better" version that adds a tool-use system prompt
       when tools are provided, that results in much better reliability when working with parallel tool calling.
 
 Recommended flags:
@@ -187,16 +187,16 @@ Known issues:
 
 VLLM provides two JSON-based chat templates for Llama 3.1 and 3.2:
 
-* <gh-file:examples/tool_chat_template_llama3.1_json.jinja> - this is the "official" chat template for the Llama 3.1
+* [examples/tool_chat_template_llama3.1_json.jinja](../../examples/tool_chat_template_llama3.1_json.jinja) - this is the "official" chat template for the Llama 3.1
 models, but tweaked so that it works better with vLLM.
-* <gh-file:examples/tool_chat_template_llama3.2_json.jinja> - this extends upon the Llama 3.1 chat template by adding support for
+* [examples/tool_chat_template_llama3.2_json.jinja](../../examples/tool_chat_template_llama3.2_json.jinja) - this extends upon the Llama 3.1 chat template by adding support for
 images.
 
 Recommended flags: `--tool-call-parser llama3_json --chat-template {see_above}`
 
 VLLM also provides a pythonic and JSON-based chat template for Llama 4, but pythonic tool calling is recommended:
 
-* <gh-file:examples/tool_chat_template_llama4_pythonic.jinja> - this is based on the [official chat template](https://www.llama.com/docs/model-cards-and-prompt-formats/llama4/) for the Llama 4 models.
+* [examples/tool_chat_template_llama4_pythonic.jinja](../../examples/tool_chat_template_llama4_pythonic.jinja) - this is based on the [official chat template](https://www.llama.com/docs/model-cards-and-prompt-formats/llama4/) for the Llama 4 models.
 
 For Llama 4 model, use `--tool-call-parser llama4_pythonic --chat-template examples/tool_chat_template_llama4_pythonic.jinja`.
 
@@ -212,7 +212,7 @@ Supported models:
 
     Recommended flags: `--tool-call-parser granite --chat-template examples/tool_chat_template_granite.jinja`
 
-    <gh-file:examples/tool_chat_template_granite.jinja>: this is a modified chat template from the original on Hugging Face. Parallel function calls are supported.
+    [examples/tool_chat_template_granite.jinja](../../examples/tool_chat_template_granite.jinja): this is a modified chat template from the original on Hugging Face. Parallel function calls are supported.
 
 * `ibm-granite/granite-3.1-8b-instruct`
 
@@ -224,7 +224,7 @@ Supported models:
 
     Recommended flags: `--tool-call-parser granite-20b-fc --chat-template examples/tool_chat_template_granite_20b_fc.jinja`
 
-    <gh-file:examples/tool_chat_template_granite_20b_fc.jinja>: this is a modified chat template from the original on Hugging Face, which is not vLLM-compatible. It blends function description elements from the Hermes template and follows the same system prompt as "Response Generation" mode from [the paper](https://arxiv.org/abs/2407.00121). Parallel function calls are supported.
+    [examples/tool_chat_template_granite_20b_fc.jinja](../../examples/tool_chat_template_granite_20b_fc.jinja): this is a modified chat template from the original on Hugging Face, which is not vLLM-compatible. It blends function description elements from the Hermes template and follows the same system prompt as "Response Generation" mode from [the paper](https://arxiv.org/abs/2407.00121). Parallel function calls are supported.
 
 ### InternLM Models (`internlm`)
 
@@ -282,8 +282,8 @@ Flags: `--tool-call-parser hermes`
 
 Supported models:
 
-* `MiniMaxAi/MiniMax-M1-40k` (use with <gh-file:examples/tool_chat_template_minimax_m1.jinja>)
-* `MiniMaxAi/MiniMax-M1-80k` (use with <gh-file:examples/tool_chat_template_minimax_m1.jinja>)
+* `MiniMaxAi/MiniMax-M1-40k` (use with [examples/tool_chat_template_minimax_m1.jinja](../../examples/tool_chat_template_minimax_m1.jinja))
+* `MiniMaxAi/MiniMax-M1-80k` (use with [examples/tool_chat_template_minimax_m1.jinja](../../examples/tool_chat_template_minimax_m1.jinja))
 
 Flags: `--tool-call-parser minimax --chat-template examples/tool_chat_template_minimax_m1.jinja`
 
@@ -291,8 +291,8 @@ Flags: `--tool-call-parser minimax --chat-template examples/tool_chat_template_m
 
 Supported models:
 
-* `deepseek-ai/DeepSeek-V3-0324` (use with <gh-file:examples/tool_chat_template_deepseekv3.jinja>)
-* `deepseek-ai/DeepSeek-R1-0528` (use with <gh-file:examples/tool_chat_template_deepseekr1.jinja>)
+* `deepseek-ai/DeepSeek-V3-0324` (use with [examples/tool_chat_template_deepseekv3.jinja](../../examples/tool_chat_template_deepseekv3.jinja))
+* `deepseek-ai/DeepSeek-R1-0528` (use with [examples/tool_chat_template_deepseekr1.jinja](../../examples/tool_chat_template_deepseekr1.jinja))
 
 Flags: `--tool-call-parser deepseek_v3 --chat-template {see_above}`
 
@@ -300,7 +300,7 @@ Flags: `--tool-call-parser deepseek_v3 --chat-template {see_above}`
 
 Supported models:
 
-* `deepseek-ai/DeepSeek-V3.1` (use with <gh-file:examples/tool_chat_template_deepseekv31.jinja>)
+* `deepseek-ai/DeepSeek-V3.1` (use with [examples/tool_chat_template_deepseekv31.jinja](../../examples/tool_chat_template_deepseekv31.jinja))
 
 Flags: `--tool-call-parser deepseek_v31 --chat-template {see_above}`
 
@@ -379,12 +379,12 @@ Limitations:
 
 Example supported models:
 
-* `meta-llama/Llama-3.2-1B-Instruct` ⚠️ (use with <gh-file:examples/tool_chat_template_llama3.2_pythonic.jinja>)
-* `meta-llama/Llama-3.2-3B-Instruct` ⚠️ (use with <gh-file:examples/tool_chat_template_llama3.2_pythonic.jinja>)
-* `Team-ACE/ToolACE-8B` (use with <gh-file:examples/tool_chat_template_toolace.jinja>)
-* `fixie-ai/ultravox-v0_4-ToolACE-8B` (use with <gh-file:examples/tool_chat_template_toolace.jinja>)
-* `meta-llama/Llama-4-Scout-17B-16E-Instruct` ⚠️ (use with <gh-file:examples/tool_chat_template_llama4_pythonic.jinja>)
-* `meta-llama/Llama-4-Maverick-17B-128E-Instruct` ⚠️ (use with <gh-file:examples/tool_chat_template_llama4_pythonic.jinja>)
+* `meta-llama/Llama-3.2-1B-Instruct` ⚠️ (use with [examples/tool_chat_template_llama3.2_pythonic.jinja](../../examples/tool_chat_template_llama3.2_pythonic.jinja))
+* `meta-llama/Llama-3.2-3B-Instruct` ⚠️ (use with [examples/tool_chat_template_llama3.2_pythonic.jinja](../../examples/tool_chat_template_llama3.2_pythonic.jinja))
+* `Team-ACE/ToolACE-8B` (use with [examples/tool_chat_template_toolace.jinja](../../examples/tool_chat_template_toolace.jinja))
+* `fixie-ai/ultravox-v0_4-ToolACE-8B` (use with [examples/tool_chat_template_toolace.jinja](../../examples/tool_chat_template_toolace.jinja))
+* `meta-llama/Llama-4-Scout-17B-16E-Instruct` ⚠️ (use with [examples/tool_chat_template_llama4_pythonic.jinja](../../examples/tool_chat_template_llama4_pythonic.jinja))
+* `meta-llama/Llama-4-Maverick-17B-128E-Instruct` ⚠️ (use with [examples/tool_chat_template_llama4_pythonic.jinja](../../examples/tool_chat_template_llama4_pythonic.jinja))
 
 Flags: `--tool-call-parser pythonic --chat-template {see_above}`
 
@@ -393,7 +393,7 @@ Flags: `--tool-call-parser pythonic --chat-template {see_above}`
 
 ## How to Write a Tool Parser Plugin
 
-A tool parser plugin is a Python file containing one or more ToolParser implementations. You can write a ToolParser similar to the `Hermes2ProToolParser` in <gh-file:vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py>.
+A tool parser plugin is a Python file containing one or more ToolParser implementations. You can write a ToolParser similar to the `Hermes2ProToolParser` in [vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py](../../vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py).
 
 Here is a summary of a plugin file: