Intern-S2-Preview - SGLang Documentation

1. Model Introduction

Intern-S2-Preview is an efficient 35B scientific multimodal foundation model. Beyond conventional parameter and data scaling, Intern-S2-Preview explores task scaling: increasing the difficulty, diversity, and coverage of scientific tasks to further unlock model capabilities. Resources:

HuggingFace: internLM/Intern-S2-Preview

2. SGLang Installation

SGLang offers multiple installation methods. Please refer to the official SGLang installation guide for installation instructions. Install SGLang from source or use an NVIDIA Docker image:

Command

# Install from source
uv pip install 'git+https://github.com/sgl-project/sglang.git#subdirectory=python'

# Or use Docker for NVIDIA GPUs
docker pull lmsysorg/sglang:latest

For how to actually launch a docker image, see Install → Method 3: Using Docker. A minimal example (substitute the inner sglang serve ... with whatever the command generator below produces):

Command

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<your-hf-token>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    sglang serve <use args below>

3. Model Deployment

3.1 Basic Configuration

Interactive Command Generator: Use the selector below to generate the deployment command for your hardware and parser configuration.

3.2 Configuration Tips

Use tp>=2 for the NVIDIA deployment commands.
Use --reasoning-parser qwen3 to separate reasoning content from final content in streaming responses.
Use --tool-call-parser qwen3_coder when serving tool-calling workloads.
Add --mamba-radix-cache-strategy extra_buffer with --speculative-algo 'NEXTN' to enable MTP.
If weight loading is slow, add --model-loader-extra-config='{"enable_multithread_load": "true", "num_threads": 64}'.

4. Model Invocation

4.1 Basic Usage

For basic API usage and request examples, see:

Basic API Usage

4.2 Advanced Usage

4.2.1 Vision Input

Intern-S2-Preview supports image inputs. Here is an example with an image:

Example

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY",
)

response = client.chat.completions.create(
    model="internLM/Intern-S2-Preview",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg"
                    },
                },
                {
                    "type": "text",
                    "text": "Describe this image in detail.",
                },
            ],
        }
    ],
    max_tokens=2048,
    stream=True,
)

thinking_started = False
has_thinking = False
has_answer = False

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta

        if hasattr(delta, "reasoning_content") and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        if delta.content:
            if has_thinking and not has_answer:
                print("\n=============== Content =================", flush=True)
                has_answer = True
            print(delta.content, end="", flush=True)

print()

4.2.2 Reasoning Parser

Enable streaming to read reasoning content separately from the final answer:

Example

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY",
)

response = client.chat.completions.create(
    model="internLM/Intern-S2-Preview",
    messages=[
        {"role": "user", "content": "Solve this step by step: What is 15% of 240?"}
    ],
    max_tokens=2048,
    stream=True,
)

thinking_started = False
has_thinking = False
has_answer = False

for chunk in response:
    if chunk.choices and len(chunk.choices) > 0:
        delta = chunk.choices[0].delta

        if hasattr(delta, "reasoning_content") and delta.reasoning_content:
            if not thinking_started:
                print("=============== Thinking =================", flush=True)
                thinking_started = True
            has_thinking = True
            print(delta.reasoning_content, end="", flush=True)

        if delta.content:
            if has_thinking and not has_answer:
                print("\n=============== Content =================", flush=True)
                has_answer = True
            print(delta.content, end="", flush=True)

print()

4.2.3 Tool Calling

Serve with --tool-call-parser qwen3_coder enabled, then send OpenAI-compatible tool requests:

Example

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="EMPTY",
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city name",
                    }
                },
                "required": ["location"],
            },
        },
    }
]

response = client.chat.completions.create(
    model="internLM/Intern-S2-Preview",
    messages=[{"role": "user", "content": "What is the weather in Beijing?"}],
    tools=tools,
    max_tokens=1024,
)

print(response.choices[0].message)

​1. Model Introduction

​2. SGLang Installation

​3. Model Deployment

​3.1 Basic Configuration

​3.2 Configuration Tips

​4. Model Invocation

​4.1 Basic Usage

​4.2 Advanced Usage

​4.2.1 Vision Input

​4.2.2 Reasoning Parser

​4.2.3 Tool Calling

1. Model Introduction

2. SGLang Installation

3. Model Deployment

3.1 Basic Configuration

3.2 Configuration Tips

4. Model Invocation

4.1 Basic Usage

4.2 Advanced Usage

4.2.1 Vision Input

4.2.2 Reasoning Parser

4.2.3 Tool Calling