add sglang support

README.md

@@ -243,6 +243,62 @@ For deployment, you can use vllm to create an OpenAI-compatible API endpoint.
 
    ```
 
+## 🚀 Deployment with SGLang (OpenAI-compatible API)
+ 1. Install SGLang latest main (choose one):
+   - **Python / pip**
+
+     ```bash
+     pip install "sglang @ git+https://github.com/sgl-project/sglang.git#subdirectory=python"
+     pip install nvidia-cudnn-cu12==9.16.0.29
+     ```
+
+   - **Docker**
+     ```bash
+      docker run --gpus all \
+      --shm-size 32g \
+      -p 30000:30000 \
+      -v ~/.cache/huggingface:/root/.cache/huggingface \
+      --ipc=host \
+      lmsysorg/sglang:latest \
+      python3 -m sglang.launch_server --model-path stepfun-ai/Step3-VL-10B --host 0.0.0.0 --port 30000
+     ```
+2. Launch the server:
+
+    ```
+    sglang serve --model-path stepfun-ai/Step3-VL-10B --trust-remote-code --port 2345 --reasoning-parser deepseek-r1 --tool-call-parser hermes
+    ```
+
+3. Call the endpoint using any OpenAI-compatible SDK (example in Python):
+    ```
+    from openai import OpenAI
+    
+    port = 30000
+
+    client = OpenAI(base_url=f"http://localhost:{port}/v1", api_key="None")
+
+    response = client.chat.completions.create(
+        model="stepfun-ai/Step3-VL-10B",
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": "What is in this image?",
+                    },
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": "https://github.com/sgl-project/sglang/blob/main/examples/assets/example_image.png?raw=true"
+                        },
+                    },
+                ],
+            }
+        ],
+    )
+
+    print(response.choices[0].message.content)
+    ```
 ## 📜 Citation
 
 If you find this project useful in your research, please cite our technical report: