[Doc] Convert docs to use colon fences (#12471)

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

docs/source/models/extensions/index.md

@@ -1,8 +1,8 @@
 # Built-in Extensions
 
-```{toctree}
+:::{toctree}
 :maxdepth: 1
 
 runai_model_streamer
 tensorizer
-```
+:::

docs/source/models/extensions/runai_model_streamer.md

@@ -48,6 +48,6 @@ You can read further about CPU buffer memory limiting [here](https://github.com/
 vllm serve /home/meta-llama/Llama-3.2-3B-Instruct --load-format runai_streamer --model-loader-extra-config '{"memory_limit":5368709120}'
 ```
 
-```{note}
+:::{note}
 For further instructions about tunable parameters and additional parameters configurable through environment variables, read the [Environment Variables Documentation](https://github.com/run-ai/runai-model-streamer/blob/master/docs/src/env-vars.md).
-```
+:::

docs/source/models/extensions/tensorizer.md

@@ -11,6 +11,6 @@ For more information on CoreWeave's Tensorizer, please refer to
 [CoreWeave's Tensorizer documentation](https://github.com/coreweave/tensorizer). For more information on serializing a vLLM model, as well a general usage guide to using Tensorizer with vLLM, see
 the [vLLM example script](https://docs.vllm.ai/en/stable/getting_started/examples/offline_inference/tensorize_vllm_model.html).
 
-```{note}
+:::{note}
 Note that to use this feature you will need to install `tensorizer` by running `pip install vllm[tensorizer]`.
-```
+:::

docs/source/models/generative_models.md

@@ -70,10 +70,10 @@ The {class}`~vllm.LLM.chat` method implements chat functionality on top of {clas
 In particular, it accepts input similar to [OpenAI Chat Completions API](https://platform.openai.com/docs/api-reference/chat)
 and automatically applies the model's [chat template](https://huggingface.co/docs/transformers/en/chat_templating) to format the prompt.
 
-```{important}
+:::{important}
 In general, only instruction-tuned models have a chat template.
 Base models may perform poorly as they are not trained to respond to the chat conversation.
-```
+:::
 
 ```python
 llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")

docs/source/models/pooling_models.md

@@ -8,54 +8,54 @@ In vLLM, pooling models implement the {class}`~vllm.model_executor.models.VllmMo
 These models use a {class}`~vllm.model_executor.layers.Pooler` to extract the final hidden states of the input
 before returning them.
 
-```{note}
+:::{note}
 We currently support pooling models primarily as a matter of convenience.
 As shown in the [Compatibility Matrix](#compatibility-matrix), most vLLM features are not applicable to
 pooling models as they only work on the generation or decode stage, so performance may not improve as much.
-```
+:::
 
 For pooling models, we support the following `--task` options.
 The selected option sets the default pooler used to extract the final hidden states:
 
-```{list-table}
+:::{list-table}
 :widths: 50 25 25 25
 :header-rows: 1
 
-* - Task
-  - Pooling Type
-  - Normalization
-  - Softmax
-* - Embedding (`embed`)
-  - `LAST`
-  - ✅︎
-  - ✗
-* - Classification (`classify`)
-  - `LAST`
-  - ✗
-  - ✅︎
-* - Sentence Pair Scoring (`score`)
-  - \*
-  - \*
-  - \*
-* - Reward Modeling (`reward`)
-  - `ALL`
-  - ✗
-  - ✗
-```
+- * Task
+  * Pooling Type
+  * Normalization
+  * Softmax
+- * Embedding (`embed`)
+  * `LAST`
+  * ✅︎
+  * ✗
+- * Classification (`classify`)
+  * `LAST`
+  * ✗
+  * ✅︎
+- * Sentence Pair Scoring (`score`)
+  * \*
+  * \*
+  * \*
+- * Reward Modeling (`reward`)
+  * `ALL`
+  * ✗
+  * ✗
+:::
 
 \*The default pooler is always defined by the model.
 
-```{note}
+:::{note}
 If the model's implementation in vLLM defines its own pooler, the default pooler is set to that instead of the one specified in this table.
-```
+:::
 
 When loading [Sentence Transformers](https://huggingface.co/sentence-transformers) models,
 we attempt to override the default pooler based on its Sentence Transformers configuration file (`modules.json`).
 
-```{tip}
+:::{tip}
 You can customize the model's pooling method via the `--override-pooler-config` option,
 which takes priority over both the model's and Sentence Transformers's defaults.
-```
+:::
 
 ## Offline Inference
 
@@ -111,10 +111,10 @@ The {class}`~vllm.LLM.score` method outputs similarity scores between sentence p
 It is primarily designed for [cross-encoder models](https://www.sbert.net/examples/applications/cross-encoder/README.html).
 These types of models serve as rerankers between candidate query-document pairs in RAG systems.
 
-```{note}
+:::{note}
 vLLM can only perform the model inference component (e.g. embedding, reranking) of RAG.
 To handle RAG at a higher level, you should use integration frameworks such as [LangChain](https://github.com/langchain-ai/langchain).
-```
+:::
 
 ```python
 llm = LLM(model="BAAI/bge-reranker-v2-m3", task="score")