[Docs] Fix syntax highlighting of shell commands (#19870)

Signed-off-by: Lukas Geiger <lukas.geiger94@gmail.com>

.buildkite/nightly-benchmarks/nightly-annotation.md

@@ -16,7 +16,7 @@ Please download the visualization scripts in the post
   - Download `nightly-benchmarks.zip`.
   - In the same folder, run the following code:
 
-  ```console
+  ```bash
   export HF_TOKEN=<your HF token>
   apt update
   apt install -y git

docs/deployment/docker.md

@@ -10,7 +10,7 @@ title: Using Docker
 vLLM offers an official Docker image for deployment.
 The image can be used to run OpenAI compatible server and is available on Docker Hub as [vllm/vllm-openai](https://hub.docker.com/r/vllm/vllm-openai/tags).
 
-```console
+```bash
 docker run --runtime nvidia --gpus all \
     -v ~/.cache/huggingface:/root/.cache/huggingface \
     --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
@@ -22,7 +22,7 @@ docker run --runtime nvidia --gpus all \
 
 This image can also be used with other container engines such as [Podman](https://podman.io/).
 
-```console
+```bash
 podman run --gpus all \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
   --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
@@ -71,7 +71,7 @@ You can add any other [engine-args][engine-args] you need after the image tag (`
 
 You can build and run vLLM from source via the provided <gh-file:docker/Dockerfile>. To build vLLM:
 
-```console
+```bash
 # optionally specifies: --build-arg max_jobs=8 --build-arg nvcc_threads=2
 DOCKER_BUILDKIT=1 docker build . \
     --target vllm-openai \
@@ -99,7 +99,7 @@ of PyTorch Nightly and should be considered **experimental**. Using the flag `--
 
 ??? Command
 
-    ```console
+    ```bash
     # Example of building on Nvidia GH200 server. (Memory usage: ~15GB, Build time: ~1475s / ~25 min, Image size: 6.93GB)
     python3 use_existing_torch.py
     DOCKER_BUILDKIT=1 docker build . \
@@ -118,7 +118,7 @@ of PyTorch Nightly and should be considered **experimental**. Using the flag `--
 
     Run the following command on your host machine to register QEMU user static handlers:
 
-    ```console
+    ```bash
     docker run --rm --privileged multiarch/qemu-user-static --reset -p yes
     ```
 
@@ -128,7 +128,7 @@ of PyTorch Nightly and should be considered **experimental**. Using the flag `--
 
 To run vLLM with the custom-built Docker image:
 
-```console
+```bash
 docker run --runtime nvidia --gpus all \
     -v ~/.cache/huggingface:/root/.cache/huggingface \
     -p 8000:8000 \

docs/deployment/frameworks/anything-llm.md

@@ -15,7 +15,7 @@ It allows you to deploy a large language model (LLM) server with vLLM as the bac
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```console
+```bash
 vllm serve Qwen/Qwen1.5-32B-Chat-AWQ --max-model-len 4096
 ```
 

docs/deployment/frameworks/autogen.md

@@ -11,7 +11,7 @@ title: AutoGen
 
 - Setup [AutoGen](https://microsoft.github.io/autogen/0.2/docs/installation/) environment
 
-```console
+```bash
 pip install vllm
 
 # Install AgentChat and OpenAI client from Extensions
@@ -23,7 +23,7 @@ pip install -U "autogen-agentchat" "autogen-ext[openai]"
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```console
+```bash
 python -m vllm.entrypoints.openai.api_server \
     --model mistralai/Mistral-7B-Instruct-v0.2
 ```

docs/deployment/frameworks/cerebrium.md

@@ -11,14 +11,14 @@ vLLM can be run on a cloud based GPU machine with [Cerebrium](https://www.cerebr
 
 To install the Cerebrium client, run:
 
-```console
+```bash
 pip install cerebrium
 cerebrium login
 ```
 
 Next, create your Cerebrium project, run:
 
-```console
+```bash
 cerebrium init vllm-project
 ```
 
@@ -58,7 +58,7 @@ Next, let us add our code to handle inference for the LLM of your choice (`mistr
 
 Then, run the following code to deploy it to the cloud:
 
-```console
+```bash
 cerebrium deploy
 ```
 

docs/deployment/frameworks/chatbox.md

@@ -15,7 +15,7 @@ It allows you to deploy a large language model (LLM) server with vLLM as the bac
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```console
+```bash
 vllm serve qwen/Qwen1.5-0.5B-Chat
 ```
 

docs/deployment/frameworks/dify.md

@@ -18,13 +18,13 @@ This guide walks you through deploying Dify using a vLLM backend.
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```console
+```bash
 vllm serve Qwen/Qwen1.5-7B-Chat
 ```
 
 - Start the Dify server with docker compose ([details](https://github.com/langgenius/dify?tab=readme-ov-file#quick-start)):
 
-```console
+```bash
 git clone https://github.com/langgenius/dify.git
 cd dify
 cd docker

docs/deployment/frameworks/dstack.md

@@ -11,14 +11,14 @@ vLLM can be run on a cloud based GPU machine with [dstack](https://dstack.ai/),
 
 To install dstack client, run:
 
-```console
+```bash
 pip install "dstack[all]
 dstack server
 ```
 
 Next, to configure your dstack project, run:
 
-```console
+```bash
 mkdir -p vllm-dstack
 cd vllm-dstack
 dstack init

docs/deployment/frameworks/haystack.md

@@ -13,7 +13,7 @@ It allows you to deploy a large language model (LLM) server with vLLM as the bac
 
 - Setup vLLM and Haystack environment
 
-```console
+```bash
 pip install vllm haystack-ai
 ```
 
@@ -21,7 +21,7 @@ pip install vllm haystack-ai
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```console
+```bash
 vllm serve mistralai/Mistral-7B-Instruct-v0.1
 ```
 

docs/deployment/frameworks/helm.md

@@ -22,7 +22,7 @@ Before you begin, ensure that you have the following:
 
 To install the chart with the release name `test-vllm`:
 
-```console
+```bash
 helm upgrade --install --create-namespace --namespace=ns-vllm test-vllm . -f values.yaml --set secrets.s3endpoint=$ACCESS_POINT --set secrets.s3bucketname=$BUCKET --set secrets.s3accesskeyid=$ACCESS_KEY --set secrets.s3accesskey=$SECRET_KEY
 ```
 
@@ -30,7 +30,7 @@ helm upgrade --install --create-namespace --namespace=ns-vllm test-vllm . -f val
 
 To uninstall the `test-vllm` deployment:
 
-```console
+```bash
 helm uninstall test-vllm --namespace=ns-vllm
 ```
 

docs/deployment/frameworks/litellm.md

@@ -18,7 +18,7 @@ And LiteLLM supports all models on VLLM.
 
 - Setup vLLM and litellm environment
 
-```console
+```bash
 pip install vllm litellm
 ```
 
@@ -28,7 +28,7 @@ pip install vllm litellm
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```console
+```bash
 vllm serve qwen/Qwen1.5-0.5B-Chat
 ```
 
@@ -56,7 +56,7 @@ vllm serve qwen/Qwen1.5-0.5B-Chat
 
 - Start the vLLM server with the supported embedding model, e.g.
 
-```console
+```bash
 vllm serve BAAI/bge-base-en-v1.5
 ```
 

docs/deployment/frameworks/open-webui.md

@@ -7,13 +7,13 @@ title: Open WebUI
 
 2. Start the vLLM server with the supported chat completion model, e.g.
 
-```console
+```bash
 vllm serve qwen/Qwen1.5-0.5B-Chat
 ```
 
 1. Start the [Open WebUI](https://github.com/open-webui/open-webui) docker container (replace the vllm serve host and vllm serve port):
 
-```console
+```bash
 docker run -d -p 3000:8080 \
 --name open-webui \
 -v open-webui:/app/backend/data \

docs/deployment/frameworks/retrieval_augmented_generation.md

@@ -15,7 +15,7 @@ Here are the integrations:
 
 - Setup vLLM and langchain environment
 
-```console
+```bash
 pip install -U vllm \
             langchain_milvus langchain_openai \
             langchain_community beautifulsoup4 \
@@ -26,14 +26,14 @@ pip install -U vllm \
 
 - Start the vLLM server with the supported embedding model, e.g.
 
-```console
+```bash
 # Start embedding service (port 8000)
 vllm serve ssmits/Qwen2-7B-Instruct-embed-base
 ```
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```console
+```bash
 # Start chat service (port 8001)
 vllm serve qwen/Qwen1.5-0.5B-Chat --port 8001
 ```
@@ -52,7 +52,7 @@ python retrieval_augmented_generation_with_langchain.py
 
 - Setup vLLM and llamaindex environment
 
-```console
+```bash
 pip install vllm \
             llama-index llama-index-readers-web \
             llama-index-llms-openai-like    \
@@ -64,14 +64,14 @@ pip install vllm \
 
 - Start the vLLM server with the supported embedding model, e.g.
 
-```console
+```bash
 # Start embedding service (port 8000)
 vllm serve ssmits/Qwen2-7B-Instruct-embed-base
 ```
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```console
+```bash
 # Start chat service (port 8001)
 vllm serve qwen/Qwen1.5-0.5B-Chat --port 8001
 ```

docs/deployment/frameworks/skypilot.md

@@ -15,7 +15,7 @@ vLLM can be **run and scaled to multiple service replicas on clouds and Kubernet
 - Check that you have installed SkyPilot ([docs](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html)).
 - Check that `sky check` shows clouds or Kubernetes are enabled.
 
-```console
+```bash
 pip install skypilot-nightly
 sky check
 ```
@@ -71,7 +71,7 @@ See the vLLM SkyPilot YAML for serving, [serving.yaml](https://github.com/skypil
 
 Start the serving the Llama-3 8B model on any of the candidate GPUs listed (L4, A10g, ...):
 
-```console
+```bash
 HF_TOKEN="your-huggingface-token" sky launch serving.yaml --env HF_TOKEN
 ```
 
@@ -83,7 +83,7 @@ Check the output of the command. There will be a shareable gradio link (like the
 
 **Optional**: Serve the 70B model instead of the default 8B and use more GPU:
 
-```console
+```bash
 HF_TOKEN="your-huggingface-token" \
   sky launch serving.yaml \
   --gpus A100:8 \
@@ -159,7 +159,7 @@ SkyPilot can scale up the service to multiple service replicas with built-in aut
 
 Start the serving the Llama-3 8B model on multiple replicas:
 
-```console
+```bash
 HF_TOKEN="your-huggingface-token" \
   sky serve up -n vllm serving.yaml \
   --env HF_TOKEN
@@ -167,7 +167,7 @@ HF_TOKEN="your-huggingface-token" \
 
 Wait until the service is ready:
 
-```console
+```bash
 watch -n10 sky serve status vllm
 ```
 
@@ -271,13 +271,13 @@ This will scale the service up to when the QPS exceeds 2 for each replica.
 
 To update the service with the new config:
 
-```console
+```bash
 HF_TOKEN="your-huggingface-token" sky serve update vllm serving.yaml --env HF_TOKEN
 ```
 
 To stop the service:
 
-```console
+```bash
 sky serve down vllm
 ```
 
@@ -317,7 +317,7 @@ It is also possible to access the Llama-3 service with a separate GUI frontend,
 
 1. Start the chat web UI:
 
-    ```console
+    ```bash
     sky launch \
       -c gui ./gui.yaml \
       --env ENDPOINT=$(sky serve status --endpoint vllm)

docs/deployment/frameworks/streamlit.md

@@ -15,13 +15,13 @@ It can be quickly integrated with vLLM as a backend API server, enabling powerfu
 
 - Start the vLLM server with the supported chat completion model, e.g.
 
-```console
+```bash
 vllm serve qwen/Qwen1.5-0.5B-Chat
 ```
 
 - Install streamlit and openai:
 
-```console
+```bash
 pip install streamlit openai
 ```
 
@@ -29,7 +29,7 @@ pip install streamlit openai
 
 - Start the streamlit web UI and start to chat:
 
-```console
+```bash
 streamlit run streamlit_openai_chatbot_webserver.py
 
 # or specify the VLLM_API_BASE or VLLM_API_KEY

docs/deployment/integrations/llamastack.md

@@ -7,7 +7,7 @@ vLLM is also available via [Llama Stack](https://github.com/meta-llama/llama-sta
 
 To install Llama Stack, run
 
-```console
+```bash
 pip install llama-stack -q
 ```
 

docs/deployment/k8s.md

@@ -115,7 +115,7 @@ Next, start the vLLM server as a Kubernetes Deployment and Service:
 
 We can verify that the vLLM server has started successfully via the logs (this might take a couple of minutes to download the model):
 
-```console
+```bash
 kubectl logs -l app.kubernetes.io/name=vllm
 ...
 INFO:     Started server process [1]
@@ -358,14 +358,14 @@ INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
 
       Apply the deployment and service configurations using `kubectl apply -f <filename>`:
 
-      ```console
+      ```bash
       kubectl apply -f deployment.yaml
       kubectl apply -f service.yaml
       ```
 
       To test the deployment, run the following `curl` command:
 
-      ```console
+      ```bash
       curl http://mistral-7b.default.svc.cluster.local/v1/completions \
         -H "Content-Type: application/json" \
         -d '{

docs/deployment/nginx.md

@@ -11,13 +11,13 @@ This document shows how to launch multiple vLLM serving containers and use Nginx
 
 This guide assumes that you have just cloned the vLLM project and you're currently in the vllm root directory.
 
-```console
+```bash
 export vllm_root=`pwd`
 ```
 
 Create a file named `Dockerfile.nginx`:
 
-```console
+```dockerfile
 FROM nginx:latest
 RUN rm /etc/nginx/conf.d/default.conf
 EXPOSE 80
@@ -26,7 +26,7 @@ CMD ["nginx", "-g", "daemon off;"]
 
 Build the container:
 
-```console
+```bash
 docker build . -f Dockerfile.nginx --tag nginx-lb
 ```
 
@@ -60,14 +60,14 @@ Create a file named `nginx_conf/nginx.conf`. Note that you can add as many serve
 
 ## Build vLLM Container
 
-```console
+```bash
 cd $vllm_root
 docker build -f docker/Dockerfile . --tag vllm
 ```
 
 If you are behind proxy, you can pass the proxy settings to the docker build command as shown below:
 
-```console
+```bash
 cd $vllm_root
 docker build \
     -f docker/Dockerfile . \
@@ -80,7 +80,7 @@ docker build \
 
 ## Create Docker Network
 
-```console
+```bash
 docker network create vllm_nginx
 ```
 
@@ -129,7 +129,7 @@ Notes:
 
 ## Launch Nginx
 
-```console
+```bash
 docker run \
     -itd \
     -p 8000:80 \
@@ -142,7 +142,7 @@ docker run \
 
 ## Verify That vLLM Servers Are Ready
 
-```console
+```bash
 docker logs vllm0 | grep Uvicorn
 docker logs vllm1 | grep Uvicorn
 ```

docs/features/multimodal_inputs.md

@@ -307,7 +307,7 @@ Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for
     By default, the timeout for fetching images through HTTP URL is `5` seconds.
     You can override this by setting the environment variable:
 
-    ```console
+    ```bash
     export VLLM_IMAGE_FETCH_TIMEOUT=<timeout>
     ```
 
@@ -370,7 +370,7 @@ Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for
     By default, the timeout for fetching videos through HTTP URL is `30` seconds.
     You can override this by setting the environment variable:
 
-    ```console
+    ```bash
     export VLLM_VIDEO_FETCH_TIMEOUT=<timeout>
     ```
 
@@ -476,7 +476,7 @@ Full example: <gh-file:examples/online_serving/openai_chat_completion_client_for
     By default, the timeout for fetching audios through HTTP URL is `10` seconds.
     You can override this by setting the environment variable:
 
-    ```console
+    ```bash
     export VLLM_AUDIO_FETCH_TIMEOUT=<timeout>
     ```
 

docs/features/quantization/auto_awq.md

@@ -9,7 +9,7 @@ The main benefits are lower latency and memory usage.
 
 You can quantize your own models by installing AutoAWQ or picking one of the [6500+ models on Huggingface](https://huggingface.co/models?search=awq).
 
-```console
+```bash
 pip install autoawq
 ```
 
@@ -43,7 +43,7 @@ After installing AutoAWQ, you are ready to quantize a model. Please refer to the
 
 To run an AWQ model with vLLM, you can use [TheBloke/Llama-2-7b-Chat-AWQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-AWQ) with the following command:
 
-```console
+```bash
 python examples/offline_inference/llm_engine_example.py \
     --model TheBloke/Llama-2-7b-Chat-AWQ \
     --quantization awq

docs/features/quantization/bitblas.md

@@ -12,7 +12,7 @@ vLLM now supports [BitBLAS](https://github.com/microsoft/BitBLAS) for more effic
 
 Below are the steps to utilize BitBLAS with vLLM.
 
-```console
+```bash
 pip install bitblas>=0.1.0
 ```
 

docs/features/quantization/bnb.md

@@ -9,7 +9,7 @@ Compared to other quantization methods, BitsAndBytes eliminates the need for cal
 
 Below are the steps to utilize BitsAndBytes with vLLM.
 
-```console
+```bash
 pip install bitsandbytes>=0.45.3
 ```
 
@@ -54,6 +54,6 @@ llm = LLM(
 
 Append the following to your model arguments for 4bit inflight quantization:
 
-```console
+```bash
 --quantization bitsandbytes
 ```

docs/features/quantization/fp8.md

@@ -23,7 +23,7 @@ The FP8 types typically supported in hardware have two distinct representations,
 
 To produce performant FP8 quantized models with vLLM, you'll need to install the [llm-compressor](https://github.com/vllm-project/llm-compressor/) library:
 
-```console
+```bash
 pip install llmcompressor
 ```
 
@@ -81,7 +81,7 @@ Since simple RTN does not require data for weight quantization and the activatio
 
 Install `vllm` and `lm-evaluation-harness` for evaluation:
 
-```console
+```bash
 pip install vllm lm-eval==0.4.4
 ```
 
@@ -99,9 +99,9 @@ Evaluate accuracy with `lm_eval` (for example on 250 samples of `gsm8k`):
 !!! note
     Quantized models can be sensitive to the presence of the `bos` token. `lm_eval` does not add a `bos` token by default, so make sure to include the `add_bos_token=True` argument when running your evaluations.
 
-```console
-$ MODEL=$PWD/Meta-Llama-3-8B-Instruct-FP8-Dynamic
-$ lm_eval \
+```bash
+MODEL=$PWD/Meta-Llama-3-8B-Instruct-FP8-Dynamic
+lm_eval \
   --model vllm \
   --model_args pretrained=$MODEL,add_bos_token=True \
   --tasks gsm8k  --num_fewshot 5 --batch_size auto --limit 250

docs/features/quantization/gguf.md

@@ -11,7 +11,7 @@ title: GGUF
 
 To run a GGUF model with vLLM, you can download and use the local GGUF model from [TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF](https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF) with the following command:
 
-```console
+```bash
 wget https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf
 # We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
 vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
@@ -20,7 +20,7 @@ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
 
 You can also add `--tensor-parallel-size 2` to enable tensor parallelism inference with 2 GPUs:
 
-```console
+```bash
 # We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
 vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
    --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
@@ -32,7 +32,7 @@ vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
 
 GGUF assumes that huggingface can convert the metadata to a config file. In case huggingface doesn't support your model you can manually create a config and pass it as hf-config-path
 
-```console
+```bash
 # If you model is not supported by huggingface you can manually provide a huggingface compatible config path
 vllm serve ./tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf \
    --tokenizer TinyLlama/TinyLlama-1.1B-Chat-v1.0 \

docs/features/quantization/gptqmodel.md

@@ -21,7 +21,7 @@ for more details on this and other advanced features.
 
 You can quantize your own models by installing [GPTQModel](https://github.com/ModelCloud/GPTQModel) or picking one of the [5000+ models on Huggingface](https://huggingface.co/models?search=gptq).
 
-```console
+```bash
 pip install -U gptqmodel --no-build-isolation -v
 ```
 
@@ -60,7 +60,7 @@ Here is an example of how to quantize `meta-llama/Llama-3.2-1B-Instruct`:
 
 To run an GPTQModel quantized model with vLLM, you can use [DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2](https://huggingface.co/ModelCloud/DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2) with the following command:
 
-```console
+```bash
 python examples/offline_inference/llm_engine_example.py \
     --model ModelCloud/DeepSeek-R1-Distill-Qwen-7B-gptqmodel-4bit-vortex-v2
 ```

docs/features/quantization/int4.md

@@ -14,13 +14,13 @@ Please visit the HF collection of [quantized INT4 checkpoints of popular LLMs re
 
 To use INT4 quantization with vLLM, you'll need to install the [llm-compressor](https://github.com/vllm-project/llm-compressor/) library:
 
-```console
+```bash
 pip install llmcompressor
 ```
 
 Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:
 
-```console
+```bash
 pip install vllm lm-eval==0.4.4
 ```
 
@@ -116,8 +116,8 @@ model = LLM("./Meta-Llama-3-8B-Instruct-W4A16-G128")
 
 To evaluate accuracy, you can use `lm_eval`:
 
-```console
-$ lm_eval --model vllm \
+```bash
+lm_eval --model vllm \
   --model_args pretrained="./Meta-Llama-3-8B-Instruct-W4A16-G128",add_bos_token=true \
   --tasks gsm8k \
   --num_fewshot 5 \

docs/features/quantization/int8.md

@@ -15,13 +15,13 @@ Please visit the HF collection of [quantized INT8 checkpoints of popular LLMs re
 
 To use INT8 quantization with vLLM, you'll need to install the [llm-compressor](https://github.com/vllm-project/llm-compressor/) library:
 
-```console
+```bash
 pip install llmcompressor
 ```
 
 Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:
 
-```console
+```bash
 pip install vllm lm-eval==0.4.4
 ```
 
@@ -122,8 +122,8 @@ model = LLM("./Meta-Llama-3-8B-Instruct-W8A8-Dynamic-Per-Token")
 
 To evaluate accuracy, you can use `lm_eval`:
 
-```console
-$ lm_eval --model vllm \
+```bash
+lm_eval --model vllm \
   --model_args pretrained="./Meta-Llama-3-8B-Instruct-W8A8-Dynamic-Per-Token",add_bos_token=true \
   --tasks gsm8k \
   --num_fewshot 5 \

docs/features/quantization/modelopt.md

@@ -4,7 +4,7 @@ The [NVIDIA TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-O
 
 We recommend installing the library with:
 
-```console
+```bash
 pip install nvidia-modelopt
 ```
 

docs/features/quantization/quantized_kvcache.md

@@ -65,7 +65,7 @@ For optimal model quality when using FP8 KV Cache, we recommend using calibrated
 
 First, install the required dependencies:
 
-```console
+```bash
 pip install llmcompressor
 ```
 

docs/features/quantization/quark.md

@@ -13,7 +13,7 @@ AWQ, GPTQ, Rotation and SmoothQuant.
 
 Before quantizing models, you need to install Quark. The latest release of Quark can be installed with pip:
 
-```console
+```bash
 pip install amd-quark
 ```
 
@@ -22,13 +22,13 @@ for more installation details.
 
 Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:
 
-```console
+```bash
 pip install vllm lm-eval==0.4.4
 ```
 
 ## Quantization Process
 
-After installing Quark, we will use an example to illustrate how to use Quark.  
+After installing Quark, we will use an example to illustrate how to use Quark.
 The Quark quantization process can be listed for 5 steps as below:
 
 1. Load the model
@@ -209,8 +209,8 @@ Now, you can load and run the Quark quantized model directly through the LLM ent
 
 Or, you can use `lm_eval` to evaluate accuracy:
 
-```console
-$ lm_eval --model vllm \
+```bash
+lm_eval --model vllm \
   --model_args pretrained=Llama-2-70b-chat-hf-w-fp8-a-fp8-kvcache-fp8-pertensor-autosmoothquant,kv_cache_dtype='fp8',quantization='quark' \
   --tasks gsm8k
 ```
@@ -222,7 +222,7 @@ to quantize large language models more conveniently. It supports quantizing mode
 of different quantization schemes and optimization algorithms. It can export the quantized model
 and run evaluation tasks on the fly. With the script, the example above can be:
 
-```console
+```bash
 python3 quantize_quark.py --model_dir meta-llama/Llama-2-70b-chat-hf \
                           --output_dir /path/to/output \
                           --quant_scheme w_fp8_a_fp8 \

docs/features/quantization/torchao.md

@@ -4,7 +4,7 @@ TorchAO is an architecture optimization library for PyTorch, it provides high pe
 
 We recommend installing the latest torchao nightly with
 
-```console
+```bash
 # Install the latest TorchAO nightly build
 # Choose the CUDA version that matches your system (cu126, cu128, etc.)
 pip install \

docs/features/tool_calling.md

@@ -351,7 +351,7 @@ Here is a summary of a plugin file:
 
 Then you can use this plugin in the command line like this.
 
-```console
+```bash
     --enable-auto-tool-choice \
     --tool-parser-plugin <absolute path of the plugin file>
     --tool-call-parser example \

docs/getting_started/installation/aws_neuron.md

@@ -26,7 +26,7 @@ The easiest way to launch a Trainium or Inferentia instance with pre-installed N
 - After launching the instance, follow the instructions in [Connect to your instance](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/AccessingInstancesLinux.html) to connect to the instance
 - Once inside your instance, activate the pre-installed virtual environment for inference by running
 
-```console
+```bash
 source /opt/aws_neuronx_venv_pytorch_2_6_nxd_inference/bin/activate
 ```
 
@@ -47,7 +47,7 @@ Currently, there are no pre-built Neuron wheels.
 
 To build and install vLLM from source, run:
 
-```console
+```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 pip install -U -r requirements/neuron.txt
@@ -66,7 +66,7 @@ Refer to [vLLM User Guide for NxD Inference](https://awsdocs-neuron.readthedocs-
 
 To install the AWS Neuron fork, run the following:
 
-```console
+```bash
 git clone -b neuron-2.23-vllm-v0.7.2 https://github.com/aws-neuron/upstreaming-to-vllm.git
 cd upstreaming-to-vllm
 pip install -r requirements/neuron.txt
@@ -100,7 +100,7 @@ to perform most of the heavy lifting which includes PyTorch model initialization
 To configure NxD Inference features through the vLLM entrypoint, use the `override_neuron_config` setting. Provide the configs you want to override
 as a dictionary (or JSON object when starting vLLM from the CLI). For example, to disable auto bucketing, include
 
-```console
+```python
 override_neuron_config={
     "enable_bucketing":False,
 }
@@ -108,7 +108,7 @@ override_neuron_config={
 
 or when launching vLLM from the CLI, pass
 
-```console
+```bash
 --override-neuron-config "{\"enable_bucketing\":false}"
 ```
 

docs/getting_started/installation/cpu.md

@@ -78,13 +78,13 @@ Currently, there are no pre-built CPU wheels.
 
 ??? Commands
 
-    ```console
-    $ docker build -f docker/Dockerfile.cpu \
+    ```bash
+    docker build -f docker/Dockerfile.cpu \
             --tag vllm-cpu-env \
             --target vllm-openai .
 
-    # Launching OpenAI server 
-    $ docker run --rm \
+    # Launching OpenAI server
+    docker run --rm \
                 --privileged=true \
                 --shm-size=4g \
                 -p 8000:8000 \
@@ -123,7 +123,7 @@ vLLM CPU backend supports the following vLLM features:
 
 - We highly recommend to use TCMalloc for high performance memory allocation and better cache locality. For example, on Ubuntu 22.4, you can run:
 
-```console
+```bash
 sudo apt-get install libtcmalloc-minimal4 # install TCMalloc library
 find / -name *libtcmalloc* # find the dynamic link library path
 export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:$LD_PRELOAD # prepend the library to LD_PRELOAD
@@ -132,7 +132,7 @@ python examples/offline_inference/basic/basic.py # run vLLM
 
 - When using the online serving, it is recommended to reserve 1-2 CPU cores for the serving framework to avoid CPU oversubscription. For example, on a platform with 32 physical CPU cores, reserving CPU 30 and 31 for the framework and using CPU 0-29 for OpenMP:
 
-```console
+```bash
 export VLLM_CPU_KVCACHE_SPACE=40
 export VLLM_CPU_OMP_THREADS_BIND=0-29
 vllm serve facebook/opt-125m
@@ -140,7 +140,7 @@ vllm serve facebook/opt-125m
 
  or using default auto thread binding:
 
-```console
+```bash
 export VLLM_CPU_KVCACHE_SPACE=40
 export VLLM_CPU_NUM_OF_RESERVED_CPU=2
 vllm serve facebook/opt-125m
@@ -189,7 +189,7 @@ vllm serve facebook/opt-125m
 
   - Tensor Parallel is supported for serving and offline inferencing. In general each NUMA node is treated as one GPU card. Below is the example script to enable Tensor Parallel = 2 for serving:
 
-    ```console
+    ```bash
     VLLM_CPU_KVCACHE_SPACE=40 VLLM_CPU_OMP_THREADS_BIND="0-31|32-63" \
         vllm serve meta-llama/Llama-2-7b-chat-hf \
         -tp=2 \
@@ -198,7 +198,7 @@ vllm serve facebook/opt-125m
 
     or using default auto thread binding:
 
-    ```console
+    ```bash
     VLLM_CPU_KVCACHE_SPACE=40 \
         vllm serve meta-llama/Llama-2-7b-chat-hf \
         -tp=2 \

docs/getting_started/installation/cpu/apple.inc.md

@@ -25,11 +25,11 @@ Currently the CPU implementation for macOS supports FP32 and FP16 datatypes.
 
 After installation of XCode and the Command Line Tools, which include Apple Clang, execute the following commands to build and install vLLM from the source.
 
-```console
+```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 pip install -r requirements/cpu.txt
-pip install -e . 
+pip install -e .
 ```
 
 !!! note

docs/getting_started/installation/cpu/build.inc.md

@@ -1,6 +1,6 @@
 First, install recommended compiler. We recommend to use `gcc/g++ >= 12.3.0` as the default compiler to avoid potential problems. For example, on Ubuntu 22.4, you can run:
 
-```console
+```bash
 sudo apt-get update  -y
 sudo apt-get install -y gcc-12 g++-12 libnuma-dev python3-dev
 sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12
@@ -8,14 +8,14 @@ sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /
 
 Second, clone vLLM project:
 
-```console
+```bash
 git clone https://github.com/vllm-project/vllm.git vllm_source
 cd vllm_source
 ```
 
 Third, install Python packages for vLLM CPU backend building:
 
-```console
+```bash
 pip install --upgrade pip
 pip install "cmake>=3.26.1" wheel packaging ninja "setuptools-scm>=8" numpy
 pip install -v -r requirements/cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu
@@ -23,13 +23,13 @@ pip install -v -r requirements/cpu.txt --extra-index-url https://download.pytorc
 
 Finally, build and install vLLM CPU backend:
 
-```console
+```bash
 VLLM_TARGET_DEVICE=cpu python setup.py install
 ```
 
 If you want to develop vllm, install it in editable mode instead.
 
-```console
+```bash
 VLLM_TARGET_DEVICE=cpu python setup.py develop
 ```
 

docs/getting_started/installation/cpu/s390x.inc.md

@@ -26,7 +26,7 @@ Currently the CPU implementation for s390x architecture supports FP32 datatype o
 
 Install the following packages from the package manager before building the vLLM. For example on RHEL 9.4:
 
-```console
+```bash
 dnf install -y \
     which procps findutils tar vim git gcc g++ make patch make cython zlib-devel \
     libjpeg-turbo-devel libtiff-devel libpng-devel libwebp-devel freetype-devel harfbuzz-devel \
@@ -35,7 +35,7 @@ dnf install -y \
 
 Install rust>=1.80 which is needed for `outlines-core` and `uvloop` python packages installation.
 
-```console
+```bash
 curl https://sh.rustup.rs -sSf | sh -s -- -y && \
     . "$HOME/.cargo/env"
 ```
@@ -45,7 +45,7 @@ Execute the following commands to build and install vLLM from the source.
 !!! tip
     Please build the following dependencies, `torchvision`, `pyarrow` from the source before building vLLM.
 
-```console
+```bash
     sed -i '/^torch/d' requirements-build.txt    # remove torch from requirements-build.txt since we use nightly builds
     pip install -v \
         --extra-index-url https://download.pytorch.org/whl/nightly/cpu \

docs/getting_started/installation/google_tpu.md

@@ -68,7 +68,7 @@ For more information about using TPUs with GKE, see:
 
 Create a TPU v5e with 4 TPU chips:
 
-```console
+```bash
 gcloud alpha compute tpus queued-resources create QUEUED_RESOURCE_ID \
   --node-id TPU_NAME \
   --project PROJECT_ID \
@@ -156,13 +156,13 @@ See [deployment-docker-pre-built-image][deployment-docker-pre-built-image] for i
 
 You can use <gh-file:docker/Dockerfile.tpu> to build a Docker image with TPU support.
 
-```console
+```bash
 docker build -f docker/Dockerfile.tpu -t vllm-tpu .
 ```
 
 Run the Docker image with the following command:
 
-```console
+```bash
 # Make sure to add `--privileged --net host --shm-size=16G`.
 docker run --privileged --net host --shm-size=16G -it vllm-tpu
 ```
@@ -185,6 +185,6 @@ docker run --privileged --net host --shm-size=16G -it vllm-tpu
 
     Install OpenBLAS with the following command:
 
-    ```console
+    ```bash
     sudo apt-get install --no-install-recommends --yes libopenblas-base libopenmpi-dev libomp-dev
     ```

docs/getting_started/installation/gpu/cuda.inc.md

@@ -22,7 +22,7 @@ Therefore, it is recommended to install vLLM with a **fresh new** environment. I
 
 You can install vLLM using either `pip` or `uv pip`:
 
-```console
+```bash
 # Install vLLM with CUDA 12.8.
 # If you are using pip.
 pip install vllm --extra-index-url https://download.pytorch.org/whl/cu128
@@ -37,7 +37,7 @@ We recommend leveraging `uv` to [automatically select the appropriate PyTorch in
 
 As of now, vLLM's binaries are compiled with CUDA 12.8 and public PyTorch release versions by default. We also provide vLLM binaries compiled with CUDA 12.6, 11.8, and public PyTorch release versions:
 
-```console
+```bash
 # Install vLLM with CUDA 11.8.
 export VLLM_VERSION=0.6.1.post1
 export PYTHON_VERSION=312
@@ -52,7 +52,7 @@ LLM inference is a fast-evolving field, and the latest code may contain bug fixe
 
 ##### Install the latest code using `pip`
 
-```console
+```bash
 pip install -U vllm \
     --pre \
     --extra-index-url https://wheels.vllm.ai/nightly
@@ -62,7 +62,7 @@ pip install -U vllm \
 
 Another way to install the latest code is to use `uv`:
 
-```console
+```bash
 uv pip install -U vllm \
     --torch-backend=auto \
     --extra-index-url https://wheels.vllm.ai/nightly
@@ -72,7 +72,7 @@ uv pip install -U vllm \
 
 If you want to access the wheels for previous commits (e.g. to bisect the behavior change, performance regression), due to the limitation of `pip`, you have to specify the full URL of the wheel file by embedding the commit hash in the URL:
 
-```console
+```bash
 export VLLM_COMMIT=33f460b17a54acb3b6cc0b03f4a17876cff5eafd # use full commit hash from the main branch
 pip install https://wheels.vllm.ai/${VLLM_COMMIT}/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl
 ```
@@ -83,7 +83,7 @@ Note that the wheels are built with Python 3.8 ABI (see [PEP 425](https://peps.p
 
 If you want to access the wheels for previous commits (e.g. to bisect the behavior change, performance regression), you can specify the commit hash in the URL:
 
-```console
+```bash
 export VLLM_COMMIT=72d9c316d3f6ede485146fe5aabd4e61dbc59069 # use full commit hash from the main branch
 uv pip install vllm \
     --torch-backend=auto \
@@ -99,7 +99,7 @@ The `uv` approach works for vLLM `v0.6.6` and later and offers an easy-to-rememb
 
 If you only need to change Python code, you can build and install vLLM without compilation. Using `pip`'s [`--editable` flag](https://pip.pypa.io/en/stable/topics/local-project-installs/#editable-installs), changes you make to the code will be reflected when you run vLLM:
 
-```console
+```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 VLLM_USE_PRECOMPILED=1 pip install --editable .
@@ -118,7 +118,7 @@ This command will do the following:
 
 In case you see an error about wheel not found when running the above command, it might be because the commit you based on in the main branch was just merged and the wheel is being built. In this case, you can wait for around an hour to try again, or manually assign the previous commit in the installation using the `VLLM_PRECOMPILED_WHEEL_LOCATION` environment variable.
 
-```console
+```bash
 export VLLM_COMMIT=72d9c316d3f6ede485146fe5aabd4e61dbc59069 # use full commit hash from the main branch
 export VLLM_PRECOMPILED_WHEEL_LOCATION=https://wheels.vllm.ai/${VLLM_COMMIT}/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl
 pip install --editable .
@@ -134,7 +134,7 @@ You can find more information about vLLM's wheels in [install-the-latest-code][i
 
 If you want to modify C++ or CUDA code, you'll need to build vLLM from source. This can take several minutes:
 
-```console
+```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 pip install -e .
@@ -160,7 +160,7 @@ There are scenarios where the PyTorch dependency cannot be easily installed via
 
 To build vLLM using an existing PyTorch installation:
 
-```console
+```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 python use_existing_torch.py
@@ -173,7 +173,7 @@ pip install --no-build-isolation -e .
 Currently, before starting the build process, vLLM fetches cutlass code from GitHub. However, there may be scenarios where you want to use a local version of cutlass instead.
 To achieve this, you can set the environment variable VLLM_CUTLASS_SRC_DIR to point to your local cutlass directory.
 
-```console
+```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 VLLM_CUTLASS_SRC_DIR=/path/to/cutlass pip install -e .
@@ -184,7 +184,7 @@ VLLM_CUTLASS_SRC_DIR=/path/to/cutlass pip install -e .
 To avoid your system being overloaded, you can limit the number of compilation jobs
 to be run simultaneously, via the environment variable `MAX_JOBS`. For example:
 
-```console
+```bash
 export MAX_JOBS=6
 pip install -e .
 ```
@@ -194,7 +194,7 @@ A side effect is a much slower build process.
 
 Additionally, if you have trouble building vLLM, we recommend using the NVIDIA PyTorch Docker image.
 
-```console
+```bash
 # Use `--ipc=host` to make sure the shared memory is large enough.
 docker run \
     --gpus all \
@@ -205,14 +205,14 @@ docker run \
 
 If you don't want to use docker, it is recommended to have a full installation of CUDA Toolkit. You can download and install it from [the official website](https://developer.nvidia.com/cuda-toolkit-archive). After installation, set the environment variable `CUDA_HOME` to the installation path of CUDA Toolkit, and make sure that the `nvcc` compiler is in your `PATH`, e.g.:
 
-```console
+```bash
 export CUDA_HOME=/usr/local/cuda
 export PATH="${CUDA_HOME}/bin:$PATH"
 ```
 
 Here is a sanity check to verify that the CUDA Toolkit is correctly installed:
 
-```console
+```bash
 nvcc --version # verify that nvcc is in your PATH
 ${CUDA_HOME}/bin/nvcc --version # verify that nvcc is in your CUDA_HOME
 ```
@@ -223,7 +223,7 @@ vLLM can fully run only on Linux but for development purposes, you can still bui
 
 Simply disable the `VLLM_TARGET_DEVICE` environment variable before installing:
 
-```console
+```bash
 export VLLM_TARGET_DEVICE=empty
 pip install -e .
 ```
@@ -238,7 +238,7 @@ See [deployment-docker-pre-built-image][deployment-docker-pre-built-image] for i
 
 Another way to access the latest code is to use the docker images:
 
-```console
+```bash
 export VLLM_COMMIT=33f460b17a54acb3b6cc0b03f4a17876cff5eafd # use full commit hash from the main branch
 docker pull public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:${VLLM_COMMIT}
 ```

docs/getting_started/installation/gpu/rocm.inc.md

@@ -31,17 +31,17 @@ Currently, there are no pre-built ROCm wheels.
 
     Alternatively, you can install PyTorch using PyTorch wheels. You can check PyTorch installation guide in PyTorch [Getting Started](https://pytorch.org/get-started/locally/). Example:
 
-    ```console
+    ```bash
     # Install PyTorch
-    $ pip uninstall torch -y
-    $ pip install --no-cache-dir --pre torch --index-url https://download.pytorch.org/whl/nightly/rocm6.3
+    pip uninstall torch -y
+    pip install --no-cache-dir --pre torch --index-url https://download.pytorch.org/whl/nightly/rocm6.3
     ```
 
 1. Install [Triton flash attention for ROCm](https://github.com/ROCm/triton)
 
     Install ROCm's Triton flash attention (the default triton-mlir branch) following the instructions from [ROCm/triton](https://github.com/ROCm/triton/blob/triton-mlir/README.md)
 
-    ```console
+    ```bash
     python3 -m pip install ninja cmake wheel pybind11
     pip uninstall -y triton
     git clone https://github.com/OpenAI/triton.git
@@ -62,7 +62,7 @@ Currently, there are no pre-built ROCm wheels.
 
     For example, for ROCm 6.3, suppose your gfx arch is `gfx90a`. To get your gfx architecture, run `rocminfo |grep gfx`.
 
-    ```console
+    ```bash
     git clone https://github.com/ROCm/flash-attention.git
     cd flash-attention
     git checkout b7d29fb
@@ -76,7 +76,7 @@ Currently, there are no pre-built ROCm wheels.
 
 3. If you choose to build AITER yourself to use a certain branch or commit, you can build AITER using the following steps:
 
-    ```console
+    ```bash
     python3 -m pip uninstall -y aiter
     git clone --recursive https://github.com/ROCm/aiter.git
     cd aiter
@@ -148,7 +148,7 @@ If you choose to build this rocm_base image yourself, the steps are as follows.
 
 It is important that the user kicks off the docker build using buildkit. Either the user put DOCKER_BUILDKIT=1 as environment variable when calling docker build command, or the user needs to setup buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:
 
-```console
+```json
 {
     "features": {
         "buildkit": true
@@ -158,7 +158,7 @@ It is important that the user kicks off the docker build using buildkit. Either
 
 To build vllm on ROCm 6.3 for MI200 and MI300 series, you can use the default:
 
-```console
+```bash
 DOCKER_BUILDKIT=1 docker build \
     -f docker/Dockerfile.rocm_base \
     -t rocm/vllm-dev:base .
@@ -169,7 +169,7 @@ DOCKER_BUILDKIT=1 docker build \
 First, build a docker image from <gh-file:docker/Dockerfile.rocm> and launch a docker container from the image.
 It is important that the user kicks off the docker build using buildkit. Either the user put `DOCKER_BUILDKIT=1` as environment variable when calling docker build command, or the user needs to setup buildkit in the docker daemon configuration /etc/docker/daemon.json as follows and restart the daemon:
 
-```console
+```bash
 {
     "features": {
         "buildkit": true
@@ -187,13 +187,13 @@ Their values can be passed in when running `docker build` with `--build-arg` opt
 
 To build vllm on ROCm 6.3 for MI200 and MI300 series, you can use the default:
 
-```console
+```bash
 DOCKER_BUILDKIT=1 docker build -f docker/Dockerfile.rocm -t vllm-rocm .
 ```
 
 To build vllm on ROCm 6.3 for Radeon RX7900 series (gfx1100), you should pick the alternative base image:
 
-```console
+```bash
 DOCKER_BUILDKIT=1 docker build \
     --build-arg BASE_IMAGE="rocm/vllm-dev:navi_base" \
     -f docker/Dockerfile.rocm \
@@ -205,7 +205,7 @@ To run the above docker image `vllm-rocm`, use the below command:
 
 ??? Command
 
-    ```console
+    ```bash
     docker run -it \
     --network=host \
     --group-add=video \

docs/getting_started/installation/gpu/xpu.inc.md

@@ -25,7 +25,7 @@ Currently, there are no pre-built XPU wheels.
 - First, install required driver and Intel OneAPI 2025.0 or later.
 - Second, install Python packages for vLLM XPU backend building:
 
-```console
+```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 pip install --upgrade pip
@@ -34,7 +34,7 @@ pip install -v -r requirements/xpu.txt
 
 - Then, build and install vLLM XPU backend:
 
-```console
+```bash
 VLLM_TARGET_DEVICE=xpu python setup.py install
 ```
 
@@ -53,9 +53,9 @@ Currently, there are no pre-built XPU images.
 # --8<-- [end:pre-built-images]
 # --8<-- [start:build-image-from-source]
 
-```console
-$ docker build -f docker/Dockerfile.xpu -t vllm-xpu-env --shm-size=4g .
-$ docker run -it \
+```bash
+docker build -f docker/Dockerfile.xpu -t vllm-xpu-env --shm-size=4g .
+docker run -it \
              --rm \
              --network=host \
              --device /dev/dri \
@@ -68,7 +68,7 @@ $ docker run -it \
 
 XPU platform supports **tensor parallel** inference/serving and also supports **pipeline parallel** as a beta feature for online serving. We require Ray as the distributed runtime backend. For example, a reference execution like following:
 
-```console
+```bash
 python -m vllm.entrypoints.openai.api_server \
      --model=facebook/opt-13b \
      --dtype=bfloat16 \

docs/getting_started/installation/intel_gaudi.md

@@ -24,7 +24,7 @@ please follow the methods outlined in the
 
 To verify that the Intel Gaudi software was correctly installed, run:
 
-```console
+```bash
 hl-smi # verify that hl-smi is in your PATH and each Gaudi accelerator is visible
 apt list --installed | grep habana # verify that habanalabs-firmware-tools, habanalabs-graph, habanalabs-rdma-core, habanalabs-thunk and habanalabs-container-runtime are installed
 pip list | grep habana # verify that habana-torch-plugin, habana-torch-dataloader, habana-pyhlml and habana-media-loader are installed
@@ -42,7 +42,7 @@ for more details.
 
 Use the following commands to run a Docker image:
 
-```console
+```bash
 docker pull vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
 docker run \
   -it \
@@ -65,7 +65,7 @@ Currently, there are no pre-built Intel Gaudi wheels.
 
 To build and install vLLM from source, run:
 
-```console
+```bash
 git clone https://github.com/vllm-project/vllm.git
 cd vllm
 pip install -r requirements/hpu.txt
@@ -74,7 +74,7 @@ python setup.py develop
 
 Currently, the latest features and performance optimizations are developed in Gaudi's [vLLM-fork](https://github.com/HabanaAI/vllm-fork) and we periodically upstream them to vLLM main repo. To install latest [HabanaAI/vLLM-fork](https://github.com/HabanaAI/vllm-fork), run the following:
 
-```console
+```bash
 git clone https://github.com/HabanaAI/vllm-fork.git
 cd vllm-fork
 git checkout habana_main
@@ -90,7 +90,7 @@ Currently, there are no pre-built Intel Gaudi images.
 
 ### Build image from source
 
-```console
+```bash
 docker build -f docker/Dockerfile.hpu -t vllm-hpu-env  .
 docker run \
   -it \

docs/getting_started/installation/python_env_setup.inc.md

@@ -1,6 +1,6 @@
 It's recommended to use [uv](https://docs.astral.sh/uv/), a very fast Python environment manager, to create and manage Python environments. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment and install vLLM using the following commands:
 
-```console
+```bash
 uv venv --python 3.12 --seed
 source .venv/bin/activate
 ```

docs/getting_started/quickstart.md

@@ -19,7 +19,7 @@ If you are using NVIDIA GPUs, you can install vLLM using [pip](https://pypi.org/
 
 It's recommended to use [uv](https://docs.astral.sh/uv/), a very fast Python environment manager, to create and manage Python environments. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment and install vLLM using the following commands:
 
-```console
+```bash
 uv venv --python 3.12 --seed
 source .venv/bin/activate
 uv pip install vllm --torch-backend=auto
@@ -29,13 +29,13 @@ uv pip install vllm --torch-backend=auto
 
 Another delightful way is to use `uv run` with `--with [dependency]` option, which allows you to run commands such as `vllm serve` without creating any permanent environment:
 
-```console
+```bash
 uv run --with vllm vllm --help
 ```
 
 You can also use [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html) to create and manage Python environments. You can install `uv` to the conda environment through `pip` if you want to manage it within the environment.
 
-```console
+```bash
 conda create -n myenv python=3.12 -y
 conda activate myenv
 pip install --upgrade uv
@@ -110,7 +110,7 @@ By default, it starts the server at `http://localhost:8000`. You can specify the
 
 Run the following command to start the vLLM server with the [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) model:
 
-```console
+```bash
 vllm serve Qwen/Qwen2.5-1.5B-Instruct
 ```
 
@@ -124,7 +124,7 @@ vllm serve Qwen/Qwen2.5-1.5B-Instruct
 
 This server can be queried in the same format as OpenAI API. For example, to list the models:
 
-```console
+```bash
 curl http://localhost:8000/v1/models
 ```
 
@@ -134,7 +134,7 @@ You can pass in the argument `--api-key` or environment variable `VLLM_API_KEY`
 
 Once your server is started, you can query the model with input prompts:
 
-```console
+```bash
 curl http://localhost:8000/v1/completions \
     -H "Content-Type: application/json" \
     -d '{
@@ -172,7 +172,7 @@ vLLM is designed to also support the OpenAI Chat Completions API. The chat inter
 
 You can use the [create chat completion](https://platform.openai.com/docs/api-reference/chat/completions/create) endpoint to interact with the model:
 
-```console
+```bash
 curl http://localhost:8000/v1/chat/completions \
     -H "Content-Type: application/json" \
     -d '{

docs/models/extensions/runai_model_streamer.md

@@ -9,27 +9,27 @@ Further reading can be found in [Run:ai Model Streamer Documentation](https://gi
 vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer.
 You first need to install vLLM RunAI optional dependency:
 
-```console
+```bash
 pip3 install vllm[runai]
 ```
 
 To run it as an OpenAI-compatible server, add the `--load-format runai_streamer` flag:
 
-```console
+```bash
 vllm serve /home/meta-llama/Llama-3.2-3B-Instruct \
     --load-format runai_streamer
 ```
 
 To run model from AWS S3 object store run:
 
-```console
+```bash
 vllm serve s3://core-llm/Llama-3-8b \
     --load-format runai_streamer
 ```
 
 To run model from a S3 compatible object store run:
 
-```console
+```bash
 RUNAI_STREAMER_S3_USE_VIRTUAL_ADDRESSING=0 \
 AWS_EC2_METADATA_DISABLED=true \
 AWS_ENDPOINT_URL=https://storage.googleapis.com \
@@ -44,7 +44,7 @@ You can tune parameters using `--model-loader-extra-config`:
 You can tune `concurrency` that controls the level of concurrency and number of OS threads reading tensors from the file to the CPU buffer.
 For reading from S3, it will be the number of client instances the host is opening to the S3 server.
 
-```console
+```bash
 vllm serve /home/meta-llama/Llama-3.2-3B-Instruct \
     --load-format runai_streamer \
     --model-loader-extra-config '{"concurrency":16}'
@@ -53,7 +53,7 @@ vllm serve /home/meta-llama/Llama-3.2-3B-Instruct \
 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
+```bash
 vllm serve /home/meta-llama/Llama-3.2-3B-Instruct \
     --load-format runai_streamer \
     --model-loader-extra-config '{"memory_limit":5368709120}'
@@ -66,13 +66,13 @@ vllm serve /home/meta-llama/Llama-3.2-3B-Instruct \
 
 vLLM also supports loading sharded models using Run:ai Model Streamer. This is particularly useful for large models that are split across multiple files. To use this feature, use the `--load-format runai_streamer_sharded` flag:
 
-```console
+```bash
 vllm serve /path/to/sharded/model --load-format runai_streamer_sharded
 ```
 
 The sharded loader expects model files to follow the same naming pattern as the regular sharded state loader: `model-rank-{rank}-part-{part}.safetensors`. You can customize this pattern using the `pattern` parameter in `--model-loader-extra-config`:
 
-```console
+```bash
 vllm serve /path/to/sharded/model \
     --load-format runai_streamer_sharded \
     --model-loader-extra-config '{"pattern":"custom-model-rank-{rank}-part-{part}.safetensors"}'
@@ -82,7 +82,7 @@ To create sharded model files, you can use the script provided in <gh-file:examp
 
 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:
 
-```console
+```bash
 vllm serve /path/to/sharded/model \
     --load-format runai_streamer_sharded \
     --model-loader-extra-config '{"concurrency":16, "memory_limit":5368709120}'

docs/models/supported_models.md

@@ -178,7 +178,7 @@ Alternatively, you can [open an issue on GitHub](https://github.com/vllm-project
 
 If you prefer, you can use the Hugging Face CLI to [download a model](https://huggingface.co/docs/huggingface_hub/guides/cli#huggingface-cli-download) or specific files from a model repository:
 
-```console
+```bash
 # Download a model
 huggingface-cli download HuggingFaceH4/zephyr-7b-beta
 
@@ -193,7 +193,7 @@ huggingface-cli download HuggingFaceH4/zephyr-7b-beta eval_results.json
 
 Use the Hugging Face CLI to [manage models](https://huggingface.co/docs/huggingface_hub/guides/manage-cache#scan-your-cache) stored in local cache:
 
-```console
+```bash
 # List cached models
 huggingface-cli scan-cache
 

docs/serving/distributed_serving.md

@@ -34,15 +34,15 @@ output = llm.generate("San Francisco is a")
 
 To run multi-GPU serving, pass in the `--tensor-parallel-size` argument when starting the server. For example, to run API server on 4 GPUs:
 
-```console
- vllm serve facebook/opt-13b \
+```bash
+vllm serve facebook/opt-13b \
      --tensor-parallel-size 4
 ```
 
 You can also additionally specify `--pipeline-parallel-size` to enable pipeline parallelism. For example, to run API server on 8 GPUs with pipeline parallelism and tensor parallelism:
 
-```console
- vllm serve gpt2 \
+```bash
+vllm serve gpt2 \
      --tensor-parallel-size 4 \
      --pipeline-parallel-size 2
 ```
@@ -55,7 +55,7 @@ The first step, is to start containers and organize them into a cluster. We have
 
 Pick a node as the head node, and run the following command:
 
-```console
+```bash
 bash run_cluster.sh \
                 vllm/vllm-openai \
                 ip_of_head_node \
@@ -66,7 +66,7 @@ bash run_cluster.sh \
 
 On the rest of the worker nodes, run the following command:
 
-```console
+```bash
 bash run_cluster.sh \
                 vllm/vllm-openai \
                 ip_of_head_node \
@@ -87,7 +87,7 @@ Then, on any node, use `docker exec -it node /bin/bash` to enter the container,
 
 After that, on any node, use `docker exec -it node /bin/bash` to enter the container again. **In the container**, you can use vLLM as usual, just as you have all the GPUs on one node: vLLM will be able to leverage GPU resources of all nodes in the Ray cluster, and therefore, only run the `vllm` command on this node but not other nodes. The common practice is to set the tensor parallel size to the number of GPUs in each node, and the pipeline parallel size to the number of nodes. For example, if you have 16 GPUs in 2 nodes (8 GPUs per node), you can set the tensor parallel size to 8 and the pipeline parallel size to 2:
 
-```console
+```bash
  vllm serve /path/to/the/model/in/the/container \
      --tensor-parallel-size 8 \
      --pipeline-parallel-size 2
@@ -95,7 +95,7 @@ After that, on any node, use `docker exec -it node /bin/bash` to enter the conta
 
 You can also use tensor parallel without pipeline parallel, just set the tensor parallel size to the number of GPUs in the cluster. For example, if you have 16 GPUs in 2 nodes (8 GPUs per node), you can set the tensor parallel size to 16:
 
-```console
+```bash
 vllm serve /path/to/the/model/in/the/container \
      --tensor-parallel-size 16
 ```

docs/serving/integrations/langchain.md

@@ -7,7 +7,7 @@ vLLM is also available via [LangChain](https://github.com/langchain-ai/langchain
 
 To install LangChain, run
 
-```console
+```bash
 pip install langchain langchain_community -q
 ```
 

docs/serving/integrations/llamaindex.md

@@ -7,7 +7,7 @@ vLLM is also available via [LlamaIndex](https://github.com/run-llama/llama_index
 
 To install LlamaIndex, run
 
-```console
+```bash
 pip install llama-index-llms-vllm -q
 ```
 

docs/usage/metrics.md

@@ -6,7 +6,7 @@ OpenAI compatible API server.
 
 You can start the server using Python, or using [Docker][deployment-docker]:
 
-```console
+```bash
 vllm serve unsloth/Llama-3.2-1B-Instruct
 ```
 

docs/usage/troubleshooting.md

@@ -127,13 +127,13 @@ If GPU/CPU communication cannot be established, you can use the following Python
 
 If you are testing with a single node, adjust `--nproc-per-node` to the number of GPUs you want to use:
 
-```console
+```bash
 NCCL_DEBUG=TRACE torchrun --nproc-per-node=<number-of-GPUs> test.py
 ```
 
 If you are testing with multi-nodes, adjust `--nproc-per-node` and `--nnodes` according to your setup and set `MASTER_ADDR` to the correct IP address of the master node, reachable from all nodes. Then, run:
 
-```console
+```bash
 NCCL_DEBUG=TRACE torchrun --nnodes 2 \
     --nproc-per-node=2 \
     --rdzv_backend=c10d \

examples/offline_inference/openai_batch/README.md

@@ -29,14 +29,14 @@ We currently support `/v1/chat/completions`, `/v1/embeddings`, and `/v1/score` e
 
 To follow along with this example, you can download the example batch, or create your own batch file in your working directory.
 
-```console
+```bash
 wget https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl
 ```
 
 Once you've created your batch file it should look like this
 
-```console
-$ cat offline_inference/openai_batch/openai_example_batch.jsonl
+```bash
+cat offline_inference/openai_batch/openai_example_batch.jsonl
 {"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
 {"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
 ```
@@ -47,7 +47,7 @@ The batch running tool is designed to be used from the command line.
 
 You can run the batch with the following command, which will write its results to a file called `results.jsonl`
 
-```console
+```bash
 python -m vllm.entrypoints.openai.run_batch \
     -i offline_inference/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
@@ -56,7 +56,7 @@ python -m vllm.entrypoints.openai.run_batch \
 
 or use command-line:
 
-```console
+```bash
 vllm run-batch \
     -i offline_inference/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
@@ -67,8 +67,8 @@ vllm run-batch \
 
 You should now have your results at `results.jsonl`. You can check your results by running `cat results.jsonl`
 
-```console
-$ cat results.jsonl
+```bash
+cat results.jsonl
 {"id":"vllm-383d1c59835645aeb2e07d004d62a826","custom_id":"request-1","response":{"id":"cmpl-61c020e54b964d5a98fa7527bfcdd378","object":"chat.completion","created":1715633336,"model":"meta-llama/Meta-Llama-3-8B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"Hello! It's great to meet you! I'm here to help with any questions or tasks you may have. What's on your mind today?"},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":25,"total_tokens":56,"completion_tokens":31}},"error":null}
 {"id":"vllm-42e3d09b14b04568afa3f1797751a267","custom_id":"request-2","response":{"id":"cmpl-f44d049f6b3a42d4b2d7850bb1e31bcc","object":"chat.completion","created":1715633336,"model":"meta-llama/Meta-Llama-3-8B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"*silence*"},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":27,"total_tokens":32,"completion_tokens":5}},"error":null}
 ```
@@ -79,7 +79,7 @@ The batch runner supports remote input and output urls that are accessible via h
 
 For example, to run against our example input file located at `https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl`, you can run
 
-```console
+```bash
 python -m vllm.entrypoints.openai.run_batch \
     -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
@@ -88,7 +88,7 @@ python -m vllm.entrypoints.openai.run_batch \
 
 or use command-line:
 
-```console
+```bash
 vllm run-batch \
     -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl \
     -o results.jsonl \
@@ -112,21 +112,21 @@ To integrate with cloud blob storage, we recommend using presigned urls.
 
 To follow along with this example, you can download the example batch, or create your own batch file in your working directory.
 
-```console
+```bash
 wget https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl
 ```
 
 Once you've created your batch file it should look like this
 
-```console
-$ cat offline_inference/openai_batch/openai_example_batch.jsonl
+```bash
+cat offline_inference/openai_batch/openai_example_batch.jsonl
 {"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
 {"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_completion_tokens": 1000}}
 ```
 
 Now upload your batch file to your S3 bucket.
 
-```console
+```bash
 aws s3 cp offline_inference/openai_batch/openai_example_batch.jsonl s3://MY_BUCKET/MY_INPUT_FILE.jsonl
 ```
 
@@ -181,7 +181,7 @@ output_url='https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AW
 
 You can now run the batch runner, using the urls generated in the previous section.
 
-```console
+```bash
 python -m vllm.entrypoints.openai.run_batch \
     -i "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_INPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
     -o "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
@@ -190,7 +190,7 @@ python -m vllm.entrypoints.openai.run_batch \
 
 or use command-line:
 
-```console
+```bash
 vllm run-batch \
     -i "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_INPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
     -o "https://s3.us-west-2.amazonaws.com/MY_BUCKET/MY_OUTPUT_FILE.jsonl?AWSAccessKeyId=ABCDEFGHIJKLMNOPQRST&Signature=abcdefghijklmnopqrstuvwxyz12345&Expires=1715800091" \
@@ -201,7 +201,7 @@ vllm run-batch \
 
 Your results are now on S3. You can view them in your terminal by running
 
-```console
+```bash
 aws s3 cp s3://MY_BUCKET/MY_OUTPUT_FILE.jsonl -
 ```
 
@@ -230,8 +230,8 @@ You can run the batch using the same command as in earlier examples.
 
 You can check your results by running `cat results.jsonl`
 
-```console
-$ cat results.jsonl
+```bash
+cat results.jsonl
 {"id":"vllm-db0f71f7dec244e6bce530e0b4ef908b","custom_id":"request-1","response":{"status_code":200,"request_id":"vllm-batch-3580bf4d4ae54d52b67eee266a6eab20","body":{"id":"embd-33ac2efa7996430184461f2e38529746","object":"list","created":444647,"model":"intfloat/e5-mistral-7b-instruct","data":[{"index":0,"object":"embedding","embedding":[0.016204833984375,0.0092010498046875,0.0018358230590820312,-0.0028228759765625,0.001422882080078125,-0.0031147003173828125,...]}],"usage":{"prompt_tokens":8,"total_tokens":8,"completion_tokens":0}}},"error":null}
 ...
 ```
@@ -261,8 +261,8 @@ You can run the batch using the same command as in earlier examples.
 
 You can check your results by running `cat results.jsonl`
 
-```console
-$ cat results.jsonl
+```bash
+cat results.jsonl
 {"id":"vllm-f87c5c4539184f618e555744a2965987","custom_id":"request-1","response":{"status_code":200,"request_id":"vllm-batch-806ab64512e44071b37d3f7ccd291413","body":{"id":"score-4ee45236897b4d29907d49b01298cdb1","object":"list","created":1737847944,"model":"BAAI/bge-reranker-v2-m3","data":[{"index":0,"object":"score","score":0.0010900497436523438},{"index":1,"object":"score","score":1.0}],"usage":{"prompt_tokens":37,"total_tokens":37,"completion_tokens":0,"prompt_tokens_details":null}}},"error":null}
 {"id":"vllm-41990c51a26d4fac8419077f12871099","custom_id":"request-2","response":{"status_code":200,"request_id":"vllm-batch-73ce66379026482699f81974e14e1e99","body":{"id":"score-13f2ffe6ba40460fbf9f7f00ad667d75","object":"list","created":1737847944,"model":"BAAI/bge-reranker-v2-m3","data":[{"index":0,"object":"score","score":0.001094818115234375},{"index":1,"object":"score","score":1.0}],"usage":{"prompt_tokens":37,"total_tokens":37,"completion_tokens":0,"prompt_tokens_details":null}}},"error":null}
 ```

examples/online_serving/opentelemetry/README.md

@@ -2,7 +2,7 @@
 
 1. Install OpenTelemetry packages:
 
-    ```console
+    ```bash
     pip install \
       'opentelemetry-sdk>=1.26.0,<1.27.0' \
       'opentelemetry-api>=1.26.0,<1.27.0' \
@@ -12,7 +12,7 @@
 
 1. Start Jaeger in a docker container:
 
-    ```console
+    ```bash
     # From: https://www.jaegertracing.io/docs/1.57/getting-started/
     docker run --rm --name jaeger \
         -e COLLECTOR_ZIPKIN_HOST_PORT=:9411 \
@@ -31,14 +31,14 @@
 
 1. In a new shell, export Jaeger IP:
 
-    ```console
+    ```bash
     export JAEGER_IP=$(docker inspect   --format '{{ .NetworkSettings.IPAddress }}' jaeger)
     export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=grpc://$JAEGER_IP:4317
     ```
 
     Then set vLLM's service name for OpenTelemetry, enable insecure connections to Jaeger and run vLLM:
 
-    ```console
+    ```bash
     export OTEL_SERVICE_NAME="vllm-server"
     export OTEL_EXPORTER_OTLP_TRACES_INSECURE=true
     vllm serve facebook/opt-125m --otlp-traces-endpoint="$OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"
@@ -46,7 +46,7 @@
 
 1. In a new shell, send requests with trace context from a dummy client
 
-    ```console
+    ```bash
     export JAEGER_IP=$(docker inspect --format '{{ .NetworkSettings.IPAddress }}' jaeger)
     export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=grpc://$JAEGER_IP:4317
     export OTEL_EXPORTER_OTLP_TRACES_INSECURE=true
@@ -67,7 +67,7 @@
 OpenTelemetry supports either `grpc` or `http/protobuf` as the transport protocol for trace data in the exporter.
 By default, `grpc` is used. To set `http/protobuf` as the protocol, configure the `OTEL_EXPORTER_OTLP_TRACES_PROTOCOL` environment variable as follows:
 
-```console
+```bash
 export OTEL_EXPORTER_OTLP_TRACES_PROTOCOL=http/protobuf
 export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://$JAEGER_IP:4318/v1/traces
 vllm serve facebook/opt-125m --otlp-traces-endpoint="$OTEL_EXPORTER_OTLP_TRACES_ENDPOINT"
@@ -79,13 +79,13 @@ OpenTelemetry allows automatic instrumentation of FastAPI.
 
 1. Install the instrumentation library
 
-    ```console
+    ```bash
     pip install opentelemetry-instrumentation-fastapi
     ```
 
 1. Run vLLM with `opentelemetry-instrument`
 
-    ```console
+    ```bash
     opentelemetry-instrument vllm serve facebook/opt-125m
     ```