[Docs] Update installation and TPU documentation to fix the render problem (#23344)

docs_new/docs/get-started/install.mdx

@@ -50,7 +50,7 @@ uv pip install "https://github.com/sgl-project/whl/releases/download/vX.Y.Z/sgla
 export TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas
 ```
 
-### **Quick fixes to common problems**
+### Quick fixes to common problems
 - 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.
@@ -109,8 +109,7 @@ Notes:
 
 Please check out [OME](https://github.com/sgl-project/ome), a Kubernetes operator for enterprise-grade management and serving of large language models (LLMs).
 
-<details>
-<summary>More</summary>
+<Accordion title="More">
 
 1. Option 1: For single node serving (typically when the model size fits into GPUs on one node)
 
@@ -120,31 +119,30 @@ Please check out [OME](https://github.com/sgl-project/ome), a Kubernetes operato
 
    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>
+</Accordion>
 
 ## Method 5: Using docker compose
 
-<details>
-<summary>More</summary>
+<Accordion title="More">
 
 > This method is recommended if you plan to serve it as a service.
 > A better approach is to use the [k8s-sglang-service.yaml](https://github.com/sgl-project/sglang/blob/main/docker/k8s-sglang-service.yaml).
 
 1. Copy the [compose.yml](https://github.com/sgl-project/sglang/blob/main/docker/compose.yaml) to your local machine
 2. Execute the command `docker compose up -d` in your terminal.
-</details>
+
+</Accordion>
 
 ## Method 6: Run on Kubernetes or Clouds with SkyPilot
 
-<details>
-<summary>More</summary>
+<Accordion title="More">
 
 To deploy on Kubernetes or 12+ clouds, you can use [SkyPilot](https://github.com/skypilot-org/skypilot).
 
 1. Install SkyPilot and set up Kubernetes cluster or cloud access: see [SkyPilot's documentation](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html).
 2. Deploy on your own infra with a single command and get the HTTP API endpoint:
-<details>
-<summary>SkyPilot YAML: <code>sglang.yaml</code></summary>
+
+<Accordion title={<>SkyPilot YAML: <code>sglang.yaml</code></>}>
 
 ```yaml Config
 # sglang.yaml
@@ -164,7 +162,7 @@ run: |
     --port 30000
 ```
 
-</details>
+</Accordion>
 
 ```bash Command
 # Deploy on any cloud or Kubernetes cluster. Use --cloud <cloud> to select a specific cloud provider.
@@ -176,12 +174,11 @@ 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>
+</Accordion>
 
 ## Method 7: Run on AWS SageMaker
 
-<details>
-<summary>More</summary>
+<Accordion title="More">
 
 To deploy on SGLang on AWS SageMaker, check out [AWS SageMaker Inference](https://aws.amazon.com/sagemaker/ai/deploy)
 
@@ -192,8 +189,7 @@ To host a model with your own container, follow the following steps:
 1. Build a docker container with [sagemaker.Dockerfile](https://github.com/sgl-project/sglang/blob/main/docker/sagemaker.Dockerfile) alongside the [serve](https://github.com/sgl-project/sglang/blob/main/docker/serve) script.
 2. Push your container onto AWS ECR.
 
-<details>
-<summary>Dockerfile Build Script: <code>build-and-push.sh</code></summary>
+<Accordion title={<>Dockerfile Build Script: <code>build-and-push.sh</code></>}>
 
 ```bash Command
 #!/bin/bash
@@ -221,7 +217,7 @@ docker push ${IMAGE_URI}
 echo "Build and push completed successfully!"
 ```
 
-</details>
+</Accordion>
 
 3. Deploy a model for serving on AWS Sagemaker, refer to [deploy_and_serve_endpoint.py](https://github.com/sgl-project/sglang/blob/main/examples/sagemaker/deploy_and_serve_endpoint.py). For more information, check out [sagemaker-python-sdk](https://github.com/aws/sagemaker-python-sdk).
    1. By default, the model server on SageMaker will run with the following command: `python3 -m sglang.launch_server --model-path opt/ml/model --host 0.0.0.0 --port 8080`. This is optimal for hosting your own model with SageMaker.
@@ -229,7 +225,7 @@ echo "Build and push completed successfully!"
    3. The serve script will automatically convert all environment variables with prefix `SM_SGLANG_` from `SM_SGLANG_INPUT_ARGUMENT` into `--input-argument` to be parsed into `python3 -m sglang.launch_server` cli.
    4. For example, to run [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) with reasoning parser, simply add additional environment variables `SM_SGLANG_MODEL_PATH=Qwen/Qwen3-0.6B` and `SM_SGLANG_REASONING_PARSER=qwen3`.
 
-</details>
+</Accordion>
 
 ## Common Notes
 

docs_new/docs/hardware-platforms/tpu.mdx

@@ -247,8 +247,7 @@ uv pip install -e "python[all]"
 
 2. Create a SkyPilot configuration file:
 
-<details>
-<summary>SkyPilot YAML: <code>sglang-jax.sky.yaml</code></summary>
+<Accordion title={<>SkyPilot YAML: <code>sglang-jax.sky.yaml</code></>}>
 
 ```yaml Config
 # sglang-jax.sky.yaml
@@ -266,7 +265,7 @@ run: |
   uv pip install -e "python[all]"
 ```
 
-</details>
+</Accordion>
 
 3. Launch your TPU cluster: