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

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

docs/models/extensions/runai_model_streamer.md

@@ -82,7 +82,7 @@ vllm serve /path/to/sharded/model \
     --model-loader-extra-config '{"pattern":"custom-model-rank-{rank}-part-{part}.safetensors"}'
 ```
 
-To create sharded model files, you can use the script provided in <gh-file:examples/offline_inference/save_sharded_state.py>. This script demonstrates how to save a model in the sharded format that is compatible with the Run:ai Model Streamer sharded loader.
+To create sharded model files, you can use the script provided in [examples/offline_inference/save_sharded_state.py](../../../examples/offline_inference/save_sharded_state.py). This script demonstrates how to save a model in the sharded format that is compatible with the Run:ai Model Streamer sharded loader.
 
 The sharded loader supports all the same tunable parameters as the regular Run:ai Model Streamer, including `concurrency` and `memory_limit`. These can be configured in the same way:
 

docs/models/generative_models.md

@@ -59,7 +59,7 @@ for output in outputs:
     By default, vLLM will use sampling parameters recommended by model creator by applying the `generation_config.json` from the huggingface model repository if it exists. In most cases, this will provide you with the best results by default if [SamplingParams][vllm.SamplingParams] is not specified.
 
     However, if vLLM's default sampling parameters are preferred, please pass `generation_config="vllm"` when creating the [LLM][vllm.LLM] instance.
-A code example can be found here: <gh-file:examples/offline_inference/basic/basic.py>
+A code example can be found here: [examples/offline_inference/basic/basic.py](../../examples/offline_inference/basic/basic.py)
 
 ### `LLM.beam_search`
 
@@ -121,7 +121,7 @@ and automatically applies the model's [chat template](https://huggingface.co/doc
         print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
     ```
 
-A code example can be found here: <gh-file:examples/offline_inference/basic/chat.py>
+A code example can be found here: [examples/offline_inference/basic/chat.py](../../examples/offline_inference/basic/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:

docs/models/pooling_models.md

@@ -9,7 +9,7 @@ before returning them.
 !!! note
     We currently support pooling models primarily as a matter of convenience. This is not guaranteed to have any performance improvement over using HF Transformers / Sentence Transformers directly.
 
-    We are now planning to optimize pooling models in vLLM. Please comment on <gh-issue:21796> if you have any suggestions!
+    We are now planning to optimize pooling models in vLLM. Please comment on <https://github.com/vllm-project/vllm/issues/21796> if you have any suggestions!
 
 ## Configuration
 
@@ -98,7 +98,7 @@ embeds = output.outputs.embedding
 print(f"Embeddings: {embeds!r} (size={len(embeds)})")
 ```
 
-A code example can be found here: <gh-file:examples/offline_inference/basic/embed.py>
+A code example can be found here: [examples/offline_inference/basic/embed.py](../../examples/offline_inference/basic/embed.py)
 
 ### `LLM.classify`
 
@@ -115,7 +115,7 @@ probs = output.outputs.probs
 print(f"Class Probabilities: {probs!r} (size={len(probs)})")
 ```
 
-A code example can be found here: <gh-file:examples/offline_inference/basic/classify.py>
+A code example can be found here: [examples/offline_inference/basic/classify.py](../../examples/offline_inference/basic/classify.py)
 
 ### `LLM.score`
 
@@ -139,7 +139,7 @@ score = output.outputs.score
 print(f"Score: {score}")
 ```
 
-A code example can be found here: <gh-file:examples/offline_inference/basic/score.py>
+A code example can be found here: [examples/offline_inference/basic/score.py](../../examples/offline_inference/basic/score.py)
 
 ### `LLM.reward`
 
@@ -156,7 +156,7 @@ data = output.outputs.data
 print(f"Data: {data!r}")
 ```
 
-A code example can be found here: <gh-file:examples/offline_inference/basic/reward.py>
+A code example can be found here: [examples/offline_inference/basic/reward.py](../../examples/offline_inference/basic/reward.py)
 
 ### `LLM.encode`
 
@@ -234,7 +234,7 @@ outputs = llm.embed(
 print(outputs[0].outputs)
 ```
 
-A code example can be found here: <gh-file:examples/offline_inference/pooling/embed_matryoshka_fy.py>
+A code example can be found here: [examples/offline_inference/pooling/embed_matryoshka_fy.py](../../examples/offline_inference/pooling/embed_matryoshka_fy.py)
 
 ### Online Inference
 
@@ -264,4 +264,4 @@ Expected output:
 {"id":"embd-5c21fc9a5c9d4384a1b021daccaf9f64","object":"list","created":1745476417,"model":"jinaai/jina-embeddings-v3","data":[{"index":0,"object":"embedding","embedding":[-0.3828125,-0.1357421875,0.03759765625,0.125,0.21875,0.09521484375,-0.003662109375,0.1591796875,-0.130859375,-0.0869140625,-0.1982421875,0.1689453125,-0.220703125,0.1728515625,-0.2275390625,-0.0712890625,-0.162109375,-0.283203125,-0.055419921875,-0.0693359375,0.031982421875,-0.04052734375,-0.2734375,0.1826171875,-0.091796875,0.220703125,0.37890625,-0.0888671875,-0.12890625,-0.021484375,-0.0091552734375,0.23046875]}],"usage":{"prompt_tokens":8,"total_tokens":8,"completion_tokens":0,"prompt_tokens_details":null}}
 ```
 
-An OpenAI client example can be found here: <gh-file:examples/online_serving/pooling/openai_embedding_matryoshka_fy.py>
+An OpenAI client example can be found here: [examples/online_serving/pooling/openai_embedding_matryoshka_fy.py](../../examples/online_serving/pooling/openai_embedding_matryoshka_fy.py)

docs/models/supported_models.md

@@ -9,7 +9,7 @@ Alongside each architecture, we include some popular models that use it.
 
 ### vLLM
 
-If vLLM natively supports a model, its implementation can be found in <gh-file:vllm/model_executor/models>.
+If vLLM natively supports a model, its implementation can be found in [vllm/model_executor/models](../../vllm/model_executor/models).
 
 These models are what we list in [supported-text-models][supported-text-models] and [supported-mm-models][supported-mm-models].
 
@@ -116,7 +116,7 @@ Here is what happens in the background when this model is loaded:
 
 1. The config is loaded.
 2. `MyModel` Python class is loaded from the `auto_map` in config, and we check that the model `is_backend_compatible()`.
-3. `MyModel` is loaded into one of the Transformers backend classes in <gh-file:vllm/model_executor/models/transformers.py> which sets `self.config._attn_implementation = "vllm"` so that vLLM's attention layer is used.
+3. `MyModel` is loaded into one of the Transformers backend classes in [vllm/model_executor/models/transformers.py](../../vllm/model_executor/models/transformers.py) which sets `self.config._attn_implementation = "vllm"` so that vLLM's attention layer is used.
 
 That's it!
 
@@ -543,7 +543,7 @@ These models primarily support the [`LLM.score`](./pooling_models.md#llmscore) A
     ```
 
 !!! note
-    Load the official original `Qwen3 Reranker` by using the following command. More information can be found at: <gh-file:examples/offline_inference/pooling/qwen3_reranker.py>.
+    Load the official original `Qwen3 Reranker` by using the following command. More information can be found at: [examples/offline_inference/pooling/qwen3_reranker.py](../../examples/offline_inference/pooling/qwen3_reranker.py).
 
     ```bash
     vllm serve Qwen/Qwen3-Reranker-0.6B --hf_overrides '{"architectures": ["Qwen3ForSequenceClassification"],"classifier_from_token": ["no", "yes"],"is_original_qwen3_reranker": true}'
@@ -581,7 +581,7 @@ These models primarily support the [`LLM.encode`](./pooling_models.md#llmencode)
 | `ModernBertForTokenClassification` | ModernBERT-based | `disham993/electrical-ner-ModernBERT-base` |  |  |
 
 !!! note
-    Named Entity Recognition (NER) usage, please refer to <gh-file:examples/offline_inference/pooling/ner.py>, <gh-file:examples/online_serving/pooling/ner_client.py>.
+    Named Entity Recognition (NER) usage, please refer to [examples/offline_inference/pooling/ner.py](../../examples/offline_inference/pooling/ner.py), [examples/online_serving/pooling/ner_client.py](../../examples/online_serving/pooling/ner_client.py).
 
 [](){ #supported-mm-models }
 
@@ -776,7 +776,7 @@ Some models are supported only via the [Transformers backend](#transformers). Th
 
 !!! note
     The official `openbmb/MiniCPM-V-2` doesn't work yet, so we need to use a fork (`HwwwH/MiniCPM-V-2`) for now.
-    For more details, please see: <gh-pr:4087#issuecomment-2250397630>
+    For more details, please see: <https://github.com/vllm-project/vllm/pull/4087#issuecomment-2250397630>
 
 !!! warning
     Our PaliGemma implementations have the same problem as Gemma 3 (see above) for both V0 and V1.
@@ -856,5 +856,5 @@ 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](gh-dir:tests) and [examples](gh-dir: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](../../tests) and [examples](../../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.