[Doc] Improve GitHub links (#11491)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/source/serving/deploying_with_docker.md

@@ -25,7 +25,7 @@ memory to share data between processes under the hood, particularly for tensor p
 
 ## Building vLLM's Docker Image from Source
 
-You can build and run vLLM from source via the provided [Dockerfile](https://github.com/vllm-project/vllm/blob/main/Dockerfile). To build vLLM:
+You can build and run vLLM from source via the provided <gh-file:Dockerfile>. To build vLLM:
 
 ```console
 $ # optionally specifies: --build-arg max_jobs=8 --build-arg nvcc_threads=2

docs/source/serving/distributed_serving.md

@@ -51,7 +51,7 @@ $     --pipeline-parallel-size 2
 
 If a single node does not have enough GPUs to hold the model, you can run the model using multiple nodes. It is important to make sure the execution environment is the same on all nodes, including the model path, the Python environment. The recommended way is to use docker images to ensure the same environment, and hide the heterogeneity of the host machines via mapping them into the same docker configuration.
 
-The first step, is to start containers and organize them into a cluster. We have provided a helper [script](https://github.com/vllm-project/vllm/tree/main/examples/run_cluster.sh) to start the cluster. Please note, this script launches docker without administrative privileges that would be required to access GPU performance counters when running profiling and tracing tools. For that purpose, the script can have `CAP_SYS_ADMIN` to the docker container by using the `--cap-add` option in the docker run command.
+The first step, is to start containers and organize them into a cluster. We have provided the helper script <gh-file:examples/run_cluster.sh> to start the cluster. Please note, this script launches docker without administrative privileges that would be required to access GPU performance counters when running profiling and tracing tools. For that purpose, the script can have `CAP_SYS_ADMIN` to the docker container by using the `--cap-add` option in the docker run command.
 
 Pick a node as the head node, and run the following command:
 
@@ -95,7 +95,7 @@ $     --tensor-parallel-size 16
 To make tensor parallel performant, you should make sure the communication between nodes is efficient, e.g. using high-speed network cards like Infiniband. To correctly set up the cluster to use Infiniband, append additional arguments like `--privileged -e NCCL_IB_HCA=mlx5` to the `run_cluster.sh` script. Please contact your system administrator for more information on how to set up the flags. One way to confirm if the Infiniband is working is to run vLLM with `NCCL_DEBUG=TRACE` environment variable set, e.g. `NCCL_DEBUG=TRACE vllm serve ...` and check the logs for the NCCL version and the network used. If you find `[send] via NET/Socket` in the logs, it means NCCL uses raw TCP Socket, which is not efficient for cross-node tensor parallel. If you find `[send] via NET/IB/GDRDMA` in the logs, it means NCCL uses Infiniband with GPU-Direct RDMA, which is efficient.
 
 ```{warning}
-After you start the Ray cluster, you'd better also check the GPU-GPU communication between nodes. It can be non-trivial to set up. Please refer to the [sanity check script](https://docs.vllm.ai/en/latest/getting_started/debugging.html) for more information. If you need to set some environment variables for the communication configuration, you can append them to the `run_cluster.sh` script, e.g. `-e NCCL_SOCKET_IFNAME=eth0`. Note that setting environment variables in the shell (e.g. `NCCL_SOCKET_IFNAME=eth0 vllm serve ...`) only works for the processes in the same node, not for the processes in the other nodes. Setting environment variables when you create the cluster is the recommended way. See the [discussion](https://github.com/vllm-project/vllm/issues/6803) for more information.
+After you start the Ray cluster, you'd better also check the GPU-GPU communication between nodes. It can be non-trivial to set up. Please refer to the [sanity check script](../getting_started/debugging.md) for more information. If you need to set some environment variables for the communication configuration, you can append them to the `run_cluster.sh` script, e.g. `-e NCCL_SOCKET_IFNAME=eth0`. Note that setting environment variables in the shell (e.g. `NCCL_SOCKET_IFNAME=eth0 vllm serve ...`) only works for the processes in the same node, not for the processes in the other nodes. Setting environment variables when you create the cluster is the recommended way. See <gh-issue:6803> for more information.
 ```
 
 ```{warning}

docs/source/serving/openai_compatible_server.md

@@ -65,8 +65,7 @@ and all chat requests will error.
 vllm serve <model> --chat-template ./path-to-chat-template.jinja
 ```
 
-vLLM community provides a set of chat templates for popular models. You can find them in the examples
-directory [here](https://github.com/vllm-project/vllm/tree/main/examples/)
+vLLM community provides a set of chat templates for popular models. You can find them under the <gh-dir:examples> directory.
 
 With the inclusion of multi-modal chat APIs, the OpenAI spec now accepts chat messages in a new format which specifies
 both a `type` and a `text` field. An example is provided below:
@@ -184,9 +183,7 @@ The order of priorities is `command line > config file values > defaults`.
 Our Completions API is compatible with [OpenAI's Completions API](https://platform.openai.com/docs/api-reference/completions);
 you can use the [official OpenAI Python client](https://github.com/openai/openai-python) to interact with it.
 
-#### Code example
-
-See [examples/openai_completion_client.py](https://github.com/vllm-project/vllm/blob/main/examples/openai_completion_client.py).
+Code example: <gh-file:examples/openai_completion_client.py>
 
 #### Extra parameters
 
@@ -217,9 +214,7 @@ We support both [Vision](https://platform.openai.com/docs/guides/vision)- and
 see our [Multimodal Inputs](../usage/multimodal_inputs.md) guide for more information.
 - *Note: `image_url.detail` parameter is not supported.*
 
-#### Code example
-
-See [examples/openai_chat_completion_client.py](https://github.com/vllm-project/vllm/blob/main/examples/openai_chat_completion_client.py).
+Code example: <gh-file:examples/openai_chat_completion_client.py>
 
 #### Extra parameters
 
@@ -252,9 +247,7 @@ which will be treated as a single prompt to the model.
 This enables multi-modal inputs to be passed to embedding models, see [this page](#multimodal-inputs) for details.
 ```
 
-#### Code example
-
-See [examples/openai_embedding_client.py](https://github.com/vllm-project/vllm/blob/main/examples/openai_embedding_client.py).
+Code example: <gh-file:examples/openai_embedding_client.py>
 
 #### Extra parameters
 
@@ -298,9 +291,7 @@ Our Pooling API encodes input prompts using a [pooling model](../models/pooling_
 
 The input format is the same as [Embeddings API](#embeddings-api), but the output data can contain an arbitrary nested list, not just a 1-D list of floats.
 
-#### Code example
-
-See [examples/openai_pooling_client.py](https://github.com/vllm-project/vllm/blob/main/examples/openai_pooling_client.py).
+Code example: <gh-file:examples/openai_pooling_client.py>
 
 (score-api)=
 ### Score API
@@ -310,9 +301,7 @@ Usually, the score for a sentence pair refers to the similarity between two sent
 
 You can find the documentation for these kind of models at [sbert.net](https://www.sbert.net/docs/package_reference/cross_encoder/cross_encoder.html).
 
-#### Code example
-
-See [examples/openai_cross_encoder_score.py](https://github.com/vllm-project/vllm/blob/main/examples/openai_cross_encoder_score.py).
+Code example: <gh-file:examples/openai_cross_encoder_score.py>
 
 #### Single inference