[Doc] Improve GitHub links (#11491)

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

docs/source/models/adding_model.md

@@ -31,8 +31,8 @@ If you don't want to fork the repository and modify vLLM's codebase, please refe
 
 ## 1. Bring your model code
 
-Clone the PyTorch model code from the HuggingFace Transformers repository and put it into the [vllm/model_executor/models](https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models) directory.
-For instance, vLLM's [OPT model](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/opt.py) was adapted from the HuggingFace's [modeling_opt.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/opt/modeling_opt.py) file.
+Clone the PyTorch model code from the HuggingFace Transformers repository and put it into the <gh-dir:vllm/model_executor/models> directory.
+For instance, vLLM's [OPT model](gh-file:vllm/model_executor/models/opt.py) was adapted from the HuggingFace's [modeling_opt.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/opt/modeling_opt.py) file.
 
 ```{warning}
 When copying the model code, make sure to review and adhere to the code's copyright and licensing terms.
@@ -99,7 +99,7 @@ Currently, vLLM supports the basic multi-head attention mechanism and its varian
 If your model employs a different attention mechanism, you will need to implement a new attention layer in vLLM.
 ```
 
-For reference, check out the [LLAMA model](https://github.com/vllm-project/vllm/blob/main/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 the [vLLM models](https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models) directory for more examples.
+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.
 
 ## 3. (Optional) Implement tensor parallelism and quantization support
 
@@ -123,7 +123,7 @@ This method should load the weights from the HuggingFace's checkpoint file and a
 
 ## 5. Register your model
 
-Finally, register your {code}`*ForCausalLM` class to the {code}`_VLLM_MODELS` in [vllm/model_executor/models/registry.py](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/registry.py).
+Finally, register your {code}`*ForCausalLM` class to the {code}`_VLLM_MODELS` in <gh-file:vllm/model_executor/models/registry.py>.
 
 ## 6. Out-of-Tree Model Integration
 

docs/source/models/enabling_multimodal_inputs.md

@@ -78,8 +78,8 @@ and register it via {meth}`INPUT_REGISTRY.register_dummy_data <vllm.inputs.regis
 
 Here are some examples:
 
-- Image inputs (static feature size): [LLaVA-1.5 Model](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llava.py)
-- Image inputs (dynamic feature size): [LLaVA-NeXT Model](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llava_next.py)
+- Image inputs (static feature size): [LLaVA-1.5 Model](gh-file:vllm/model_executor/models/llava.py)
+- Image inputs (dynamic feature size): [LLaVA-NeXT Model](gh-file:vllm/model_executor/models/llava_next.py)
 
 ```{seealso}
 [Input Processing Pipeline](#input-processing-pipeline)
@@ -107,8 +107,8 @@ The dummy data should have the maximum possible number of multi-modal tokens, as
 
 Here are some examples:
 
-- Image inputs (static feature size): [LLaVA-1.5 Model](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llava.py)
-- Image inputs (dynamic feature size): [LLaVA-NeXT Model](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llava_next.py)
+- Image inputs (static feature size): [LLaVA-1.5 Model](gh-file:vllm/model_executor/models/llava.py)
+- Image inputs (dynamic feature size): [LLaVA-NeXT Model](gh-file:vllm/model_executor/models/llava_next.py)
 
 ```{seealso}
 [Input Processing Pipeline](#input-processing-pipeline)
@@ -135,8 +135,8 @@ You can register input processors via {meth}`INPUT_REGISTRY.register_input_proce
 A common use case of input processors is inserting placeholder tokens to leverage the vLLM framework for attention mask generation.
 Here are some examples:
 
-- Insert static number of image tokens: [LLaVA-1.5 Model](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llava.py)
-- Insert dynamic number of image tokens: [LLaVA-NeXT Model](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llava_next.py)
+- Insert static number of image tokens: [LLaVA-1.5 Model](gh-file:vllm/model_executor/models/llava.py)
+- Insert dynamic number of image tokens: [LLaVA-NeXT Model](gh-file:vllm/model_executor/models/llava_next.py)
 
 ```{seealso}
 [Input Processing Pipeline](#input-processing-pipeline)

docs/source/models/generative_models.md

@@ -46,7 +46,7 @@ for output in outputs:
     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
 ```
 
-A code example can be found in [examples/offline_inference.py](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference.py).
+A code example can be found here: <gh-file:examples/offline_inference.py>
 
 ### `LLM.beam_search`
 
@@ -103,7 +103,7 @@ for output in outputs:
     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
 ```
 
-A code example can be found in [examples/offline_inference_chat.py](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_chat.py).
+A code example can be found here: <gh-file:examples/offline_inference_chat.py>
 
 If the model doesn't have a chat template or you want to specify another one,
 you can explicitly pass a chat template:
@@ -120,7 +120,7 @@ outputs = llm.chat(conversation, chat_template=custom_template)
 
 ## Online Inference
 
-Our [OpenAI Compatible Server](../serving/openai_compatible_server) provides endpoints that correspond to the offline APIs:
+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](#multimodal-inputs) for models with a chat template.

docs/source/models/pooling_models.md

@@ -65,7 +65,7 @@ embeds = output.outputs.embedding
 print(f"Embeddings: {embeds!r} (size={len(embeds)})")
 ```
 
-A code example can be found in [examples/offline_inference_embedding.py](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_embedding.py).
+A code example can be found here: <gh-file:examples/offline_inference_embedding.py>
 
 ### `LLM.classify`
 
@@ -80,7 +80,7 @@ probs = output.outputs.probs
 print(f"Class Probabilities: {probs!r} (size={len(probs)})")
 ```
 
-A code example can be found in [examples/offline_inference_classification.py](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_classification.py).
+A code example can be found here: <gh-file:examples/offline_inference_classification.py>
 
 ### `LLM.score`
 
@@ -102,7 +102,7 @@ score = output.outputs.score
 print(f"Score: {score}")
 ```
 
-A code example can be found in [examples/offline_inference_scoring.py](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_scoring.py).
+A code example can be found here: <gh-file:examples/offline_inference_scoring.py>
 
 ## Online Inference
 

docs/source/models/supported_models.md

@@ -756,7 +756,7 @@ and pass {code}`--hf_overrides '{"architectures": ["MantisForConditionalGenerati
 
 ```{note}
 The official {code}`openbmb/MiniCPM-V-2` doesn't work yet, so we need to use a fork ({code}`HwwwH/MiniCPM-V-2`) for now.
-For more details, please see: <https://github.com/vllm-project/vllm/pull/4087#issuecomment-2250397630>
+For more details, please see: <gh-pr:4087#issuecomment-2250397630>
 ```
 
 ### Pooling Models
@@ -834,5 +834,5 @@ We have the following levels of testing for models:
 
 1. **Strict Consistency**: We compare the output of the model with the output of the model in the HuggingFace Transformers library under greedy decoding. This is the most stringent test. Please refer to [models tests](https://github.com/vllm-project/vllm/blob/main/tests/models) for the models that have passed this test.
 2. **Output Sensibility**: We check if the output of the model is sensible and coherent, by measuring the perplexity of the output and checking for any obvious errors. This is a less stringent test.
-3. **Runtime Functionality**: We check if the model can be loaded and run without errors. This is the least stringent test. Please refer to [functionality tests](https://github.com/vllm-project/vllm/tree/main/tests) and [examples](https://github.com/vllm-project/vllm/tree/main/examples) for the models that have passed this test.
+3. **Runtime Functionality**: We check if the model can be loaded and run without errors. This is the least stringent test. Please refer to [functionality tests](gh-dir:tests) and [examples](gh-dir:main/examples) for the models that have passed this test.
 4. **Community Feedback**: We rely on the community to provide feedback on the models. If a model is broken or not working as expected, we encourage users to raise issues to report it or open pull requests to fix it. The rest of the models fall under this category.