[CI/Build] Add markdown linter (#11857)

Signed-off-by: Rafael Vasquez <rafvasq21@gmail.com>

docs/source/models/extensions/runai_model_streamer.md

@@ -9,25 +9,25 @@ vLLM supports loading weights in Safetensors format using the Run:ai Model Strea
 You first need to install vLLM RunAI optional dependency:
 
 ```console
-$ pip3 install vllm[runai]
+pip3 install vllm[runai]
 ```
 
 To run it as an OpenAI-compatible server, add the `--load-format runai_streamer` flag:
 
 ```console
-$ vllm serve /home/meta-llama/Llama-3.2-3B-Instruct --load-format runai_streamer
+vllm serve /home/meta-llama/Llama-3.2-3B-Instruct --load-format runai_streamer
 ```
 
 To run model from AWS S3 object store run:
 
 ```console
-$ vllm serve s3://core-llm/Llama-3-8b --load-format runai_streamer
+vllm serve s3://core-llm/Llama-3-8b --load-format runai_streamer
 ```
 
 To run model from a S3 compatible object store run:
 
 ```console
-$ RUNAI_STREAMER_S3_USE_VIRTUAL_ADDRESSING=0 AWS_EC2_METADATA_DISABLED=true AWS_ENDPOINT_URL=https://storage.googleapis.com vllm serve s3://core-llm/Llama-3-8b --load-format runai_streamer
+RUNAI_STREAMER_S3_USE_VIRTUAL_ADDRESSING=0 AWS_EC2_METADATA_DISABLED=true AWS_ENDPOINT_URL=https://storage.googleapis.com vllm serve s3://core-llm/Llama-3-8b --load-format runai_streamer
 ```
 
 ## Tunable parameters
@@ -38,14 +38,14 @@ You can tune `concurrency` that controls the level of concurrency and number of
 For reading from S3, it will be the number of client instances the host is opening to the S3 server.
 
 ```console
-$ vllm serve /home/meta-llama/Llama-3.2-3B-Instruct --load-format runai_streamer --model-loader-extra-config '{"concurrency":16}'
+vllm serve /home/meta-llama/Llama-3.2-3B-Instruct --load-format runai_streamer --model-loader-extra-config '{"concurrency":16}'
 ```
 
 You can control the size of the CPU Memory buffer to which tensors are read from the file, and limit this size.
 You can read further about CPU buffer memory limiting [here](https://github.com/run-ai/runai-model-streamer/blob/master/docs/src/env-vars.md#runai_streamer_memory_limit).
 
 ```console
-$ vllm serve /home/meta-llama/Llama-3.2-3B-Instruct --load-format runai_streamer --model-loader-extra-config '{"memory_limit":5368709120}'
+vllm serve /home/meta-llama/Llama-3.2-3B-Instruct --load-format runai_streamer --model-loader-extra-config '{"memory_limit":5368709120}'
 ```
 
 ```{note}

docs/source/models/supported_models.md

@@ -45,7 +45,7 @@ Alternatively, you can [open an issue on GitHub](https://github.com/vllm-project
 To use models from [ModelScope](https://www.modelscope.cn) instead of HuggingFace Hub, set an environment variable:
 
 ```shell
-$ export VLLM_USE_MODELSCOPE=True
+export VLLM_USE_MODELSCOPE=True
 ```
 
 And use with `trust_remote_code=True`.
@@ -820,19 +820,22 @@ The following table lists those that are tested in vLLM.
 
 _________________
 
-# Model Support Policy
+## Model Support Policy
 
 At vLLM, we are committed to facilitating the integration and support of third-party models within our ecosystem. Our approach is designed to balance the need for robustness and the practical limitations of supporting a wide range of models. Here’s how we manage third-party model support:
 
 1. **Community-Driven Support**: We encourage community contributions for adding new models. When a user requests support for a new model, we welcome pull requests (PRs) from the community. These contributions are evaluated primarily on the sensibility of the output they generate, rather than strict consistency with existing implementations such as those in transformers. **Call for contribution:** PRs coming directly from model vendors are greatly appreciated!
+
 2. **Best-Effort Consistency**: While we aim to maintain a level of consistency between the models implemented in vLLM and other frameworks like transformers, complete alignment is not always feasible. Factors like acceleration techniques and the use of low-precision computations can introduce discrepancies. Our commitment is to ensure that the implemented models are functional and produce sensible results.
 
-```{tip}
-When comparing the output of `model.generate` from HuggingFace Transformers with the output of `llm.generate` from vLLM, note that the former reads the model's generation config file (i.e., [generation_config.json](https://github.com/huggingface/transformers/blob/19dabe96362803fb0a9ae7073d03533966598b17/src/transformers/generation/utils.py#L1945)) and applies the default parameters for generation, while the latter only uses the parameters passed to the function. Ensure all sampling parameters are identical when comparing outputs.
-```
+    ```{tip}
+    When comparing the output of `model.generate` from HuggingFace Transformers with the output of `llm.generate` from vLLM, note that the former reads the model's generation config file (i.e., [generation_config.json](https://github.com/huggingface/transformers/blob/19dabe96362803fb0a9ae7073d03533966598b17/src/transformers/generation/utils.py#L1945)) and applies the default parameters for generation, while the latter only uses the parameters passed to the function. Ensure all sampling parameters are identical when comparing outputs.
+    ```
 
 3. **Issue Resolution and Model Updates**: Users are encouraged to report any bugs or issues they encounter with third-party models. Proposed fixes should be submitted via PRs, with a clear explanation of the problem and the rationale behind the proposed solution. If a fix for one model impacts another, we rely on the community to highlight and address these cross-model dependencies. Note: for bugfix PRs, it is good etiquette to inform the original author to seek their feedback.
+
 4. **Monitoring and Updates**: Users interested in specific models should monitor the commit history for those models (e.g., by tracking changes in the main/vllm/model_executor/models directory). This proactive approach helps users stay informed about updates and changes that may affect the models they use.
+
 5. **Selective Focus**: Our resources are primarily directed towards models with significant user interest and impact. Models that are less frequently used may receive less attention, and we rely on the community to play a more active role in their upkeep and improvement.
 
 Through this approach, vLLM fosters a collaborative environment where both the core development team and the broader community contribute to the robustness and diversity of the third-party models supported in our ecosystem.