diff --git a/docs/start/install.md b/docs/start/install.md
index 62a469271..fb3b9f100 100644
--- a/docs/start/install.md
+++ b/docs/start/install.md
@@ -19,13 +19,15 @@ uv pip install "sglang[all]>=0.4.5" --find-links https://flashinfer.ai/whl/cu124
 - SGLang currently uses torch 2.5, so you need to install flashinfer for torch 2.5. If you want to install flashinfer separately, please refer to [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html). Please note that the FlashInfer pypi package is called `flashinfer-python` instead of `flashinfer`.
 
 - If you encounter `OSError: CUDA_HOME environment variable is not set`. Please set it to your CUDA install root with either of the following solutions:
+
   1. Use `export CUDA_HOME=/usr/local/cuda-<your-cuda-version>` to set the `CUDA_HOME` environment variable.
   2. Install FlashInfer first following [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html), then install SGLang as described above.
 
 - If you encounter `ImportError; cannot import name 'is_valid_list_of_images' from 'transformers.models.llama.image_processing_llama'`, try to use the specified version of `transformers` in [pyproject.toml](https://github.com/sgl-project/sglang/blob/main/python/pyproject.toml). Currently, just running `pip install transformers==4.48.3`.
 
 ## Method 2: From source
-```
+
+```bash
 # Use the last release branch
 git clone -b v0.4.5 https://github.com/sgl-project/sglang.git
 cd sglang
@@ -40,7 +42,7 @@ If you want to develop SGLang, it is recommended to use docker. Please refer to
 
 Note: For AMD ROCm system with Instinct/MI GPUs, do following instead:
 
-```
+```bash
 # Use the last release branch
 git clone -b v0.4.5 https://github.com/sgl-project/sglang.git
 cd sglang
@@ -53,6 +55,7 @@ pip install -e "python[all_hip]"
 ```
 
 ## Method 3: Using docker
+
 The docker images are available on Docker Hub as [lmsysorg/sglang](https://hub.docker.com/r/lmsysorg/sglang/tags), built from [Dockerfile](https://github.com/sgl-project/sglang/tree/main/docker).
 Replace `<secret>` below with your huggingface hub [token](https://huggingface.co/docs/hub/en/security-tokens).
 
@@ -104,14 +107,15 @@ drun v0.4.5-rocm630 python3 -m sglang.bench_one_batch --batch-size 32 --input 10
 <summary>More</summary>
 
 1. Option 1: For single node serving (typically when the model size fits into GPUs on one node)
+
    Execute command `kubectl apply -f docker/k8s-sglang-service.yaml`, to create k8s deployment and service, with llama-31-8b as example.
 
 2. Option 2: For multi-node serving (usually when a large model requires more than one GPU node, such as `DeepSeek-R1`)
+
    Modify the LLM model path and arguments as necessary, then execute command `kubectl apply -f docker/k8s-sglang-distributed-sts.yaml`, to create two nodes k8s statefulset and serving service.
+
 </details>
 
-
-
 ## Method 6: Run on Kubernetes or Clouds with SkyPilot
 
 <details>
@@ -141,6 +145,7 @@ run: |
     --host 0.0.0.0 \
     --port 30000
 ```
+
 </details>
 
 ```bash
@@ -150,10 +155,12 @@ HF_TOKEN=<secret> sky launch -c sglang --env HF_TOKEN sglang.yaml
 # Get the HTTP API endpoint
 sky status --endpoint 30000 sglang
 ```
+
 3. To further scale up your deployment with autoscaling and failure recovery, check out the [SkyServe + SGLang guide](https://github.com/skypilot-org/skypilot/tree/master/llm/sglang#serving-llama-2-with-sglang-for-more-traffic-using-skyserve).
 </details>
 
 ## Common Notes
+
 - [FlashInfer](https://github.com/flashinfer-ai/flashinfer) is the default attention kernel backend. It only supports sm75 and above. If you encounter any FlashInfer-related issues on sm75+ devices (e.g., T4, A10, A100, L4, L40S, H100), please switch to other kernels by adding `--attention-backend triton --sampling-backend pytorch` and open an issue on GitHub.
 - If you only need to use OpenAI models with the frontend language, you can avoid installing other dependencies by using `pip install "sglang[openai]"`.
 - The language frontend operates independently of the backend runtime. You can install the frontend locally without needing a GPU, while the backend can be set up on a GPU-enabled machine. To install the frontend, run `pip install sglang`, and for the backend, use `pip install sglang[srt]`. `srt` is the abbreviation of SGLang runtime.