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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:

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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>

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3. Model Deployment

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3.1 Basic Configuration

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

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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-scheduler-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}'.

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4. Model Invocation

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4.1 Basic Usage

For basic API usage and request examples, see:

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4.2 Advanced Usage

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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()

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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()

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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)