Intern-S2-397B-FP8

Intern-S2-397B

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Introduction

We introduce Intern-S2-397B, our most capable multimodal foundation model for scientific intelligence and long-horizon agents. Intern-S2-397B scales along three critical dimensions: pre-training, reinforcement-learning task coverage, and interactive agent environments. By combining a new vision-language pre-training paradigm with large-scale multi-task reinforcement learning and long-horizon agent reinforcement learning, Intern-S2-397B delivers a step change in general reasoning, scientific problem solving, and agentic capabilities.

Features

• New Pre-training Paradigm. Via visual pretraining, Intern-S2-397B learns directly from raw pages of scientific literature, jointly modeling symbolic semantics and visual relationships in a shared representation space without intermediate parsing. This preserves text-visual correspondence, strengthens spatial and visual reasoning, and improves data efficiency.

• Scientific Modality Reasoning and Generation. By scaling diverse scientific reinforcement-learning tasks across more than 20 domains and training them jointly, Intern-S2-397B achieves leading general-reasoning performance among open-source models and strong results in specialized scientific tasks such as biomolecular interaction design and material structure generation.

• General & Scientific Long-Horizon Agents. By connecting multiple agent frameworks to large-scale sandboxed environments for black-box agentic reinforcement learning, Intern-S2-397B improves generalization and raises the capability ceiling for long-horizon tasks in both general and scientific domains.

Performance

We evaluate the Intern-S2-397B on various benchmarks, including general datasets and scientific datasets. We report the performance comparison with the recent VLMs and LLMs below.

Note: <u>Underline</u> means the best performance among open-sourced models, Bold indicates the best performance among all models.

We use the OpenCompass, VLMEvalKit, and AgentCompass to evaluate all models. For text reasoning benchmarks, Intern-S2-397B is evaluated with a maximum inference length of 256K tokens, while for multimodal benchmarks, it is evaluated with a maximum inference length of 64K tokens.

Quick Start

Sampling Parameters

We recommend using the following hyperparameters to ensure better results

top_p = 0.95
top_k = 50
min_p = 0.0
temperature = 0.8

Serving

Intern-S2-397B can be deployed using any of the following LLM inference frameworks:

• LMDeploy
• vLLM
• SGLang

Detailed deployment examples for these frameworks are available in the Model Deployment Guide.

Advanced Usage

Tool Calling

Tool Calling lets the model extend its capabilities by invoking external tools and APIs. The example below shows how to use it to fetch the latest weather forecast via an OpenAI-compatible API (based on lmdeploy api server).

from openai import OpenAI
import json


def get_current_temperature(location: str, unit: str = "celsius"):
    """Get current temperature at a location.

    Args:
        location: The location to get the temperature for, in the format "City, State, Country".
        unit: The unit to return the temperature in. Defaults to "celsius". (choices: ["celsius", "fahrenheit"])

    Returns:
        the temperature, the location, and the unit in a dict
    """
    return {
        "temperature": 26.1,
        "location": location,
        "unit": unit,
    }


def get_temperature_date(location: str, date: str, unit: str = "celsius"):
    """Get temperature at a location and date.

    Args:
        location: The location to get the temperature for, in the format "City, State, Country".
        date: The date to get the temperature for, in the format "Year-Month-Day".
        unit: The unit to return the temperature in. Defaults to "celsius". (choices: ["celsius", "fahrenheit"])

    Returns:
        the temperature, the location, the date and the unit in a dict
    """
    return {
        "temperature": 25.9,
        "location": location,
        "date": date,
        "unit": unit,
    }

def get_function_by_name(name):
    if name == "get_current_temperature":
        return get_current_temperature
    if name == "get_temperature_date":
        return get_temperature_date

tools = [{
    'type': 'function',
    'function': {
        'name': 'get_current_temperature',
        'description': 'Get current temperature at a location.',
        'parameters': {
            'type': 'object',
            'properties': {
                'location': {
                    'type': 'string',
                    'description': 'The location to get the temperature for, in the format \'City, State, Country\'.'
                },
                'unit': {
                    'type': 'string',
                    'enum': [
                        'celsius',
                        'fahrenheit'
                    ],
                    'description': 'The unit to return the temperature in. Defaults to \'celsius\'.'
                }
            },
            'required': [
                'location'
            ]
        }
    }
}, {
    'type': 'function',
    'function': {
        'name': 'get_temperature_date',
        'description': 'Get temperature at a location and date.',
        'parameters': {
            'type': 'object',
            'properties': {
                'location': {
                    'type': 'string',
                    'description': 'The location to get the temperature for, in the format \'City, State, Country\'.'
                },
                'date': {
                    'type': 'string',
                    'description': 'The date to get the temperature for, in the format \'Year-Month-Day\'.'
                },
                'unit': {
                    'type': 'string',
                    'enum': [
                        'celsius',
                        'fahrenheit'
                    ],
                    'description': 'The unit to return the temperature in. Defaults to \'celsius\'.'
                }
            },
            'required': [
                'location',
                'date'
            ]
        }
    }
}]



messages = [
    {'role': 'user', 'content': 'Today is 2024-11-14, What\'s the temperature in San Francisco now? How about tomorrow?'}
]

openai_api_key = "EMPTY"
openai_api_base = "http://0.0.0.0:23333/v1"
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)
model_name = "internlm/Intern-S2-397B"  # Must match the model ID served by your deployment.
response = client.chat.completions.create(
    model=model_name,
    messages=messages,
    max_tokens=32768,
    temperature=0.8,
    top_p=0.95,
    extra_body=dict(spaces_between_special_tokens=False),
    tools=tools)
print(response.choices[0].message)
messages.append(response.choices[0].message)

for tool_call in response.choices[0].message.tool_calls:
    tool_call_args = json.loads(tool_call.function.arguments)
    tool_call_result = get_function_by_name(tool_call.function.name)(**tool_call_args)
    tool_call_result = json.dumps(tool_call_result, ensure_ascii=False)
    messages.append({
        'role': 'tool',
        'name': tool_call.function.name,
        'content': tool_call_result,
        'tool_call_id': tool_call.id
    })

response = client.chat.completions.create(
    model=model_name,
    messages=messages,
    temperature=0.8,
    top_p=0.95,
    extra_body=dict(spaces_between_special_tokens=False),
    tools=tools)
print(response.choices[0].message)

Switching Between Thinking and Non-Thinking Modes

Intern-S2-397B enables thinking mode by default, enhancing the model's reasoning capabilities to generate higher-quality responses. This feature can be disabled by setting enable_thinking=False in tokenizer.apply_chat_template

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False  # think mode indicator
)

When serving Intern-S2-397B models, you can dynamically control the thinking mode by adjusting the enable_thinking parameter in your requests.

from openai import OpenAI
import json

messages = [
{
    'role': 'user',
    'content': 'who are you'
}, {
    'role': 'assistant',
    'content': 'I am an AI'
}, {
    'role': 'user',
    'content': 'AGI is?'
}]

openai_api_key = "EMPTY"
openai_api_base = "http://0.0.0.0:23333/v1"
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)
model_name = "internlm/Intern-S2-397B"  # Must match the model ID served by your deployment.

response = client.chat.completions.create(
    model=model_name,
    messages=messages,
    temperature=0.8,
    top_p=0.95,
    max_tokens=2048,
    extra_body={
        "chat_template_kwargs": {"enable_thinking": False}
    }
)
print(json.dumps(response.model_dump(), indent=2, ensure_ascii=False))

Note: We do not recommend disabling thinking mode for agentic tasks.

Time Series Demo

Time series inference is currently only supported in LMDeploy. To get started, download and deploy Intern-S2-397B with LMDeploy by following the Model Deployment Guide. Below is an example of detecting earthquake events from a time series signal file. Additional data types and functionalities are also supported.

Please note: in the message content, the order of time_series_url and the text prompt can be arbitrary.

from openai import OpenAI
from lmdeploy.vl.utils import encode_time_series_base64

openai_api_key = "EMPTY"
openai_api_base = "http://0.0.0.0:8000/v1"
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)
model_name = "internlm/Intern-S2-397B"  # Must match the model ID served by your deployment.


def send_base64(file_path: str, sampling_rate: int = 100):
    """base64-encoded time-series data."""

    # encode_time_series_base64 accepts local file paths and http urls,
    # encoding time-series data (.npy, .csv, .wav, .mp3, .flac, etc.) into base64 strings.
    base64_ts = encode_time_series_base64(file_path)

    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "time_series_url",
                    "time_series_url": {
                        "url": f"data:time_series/npy;base64,{base64_ts}",
                        "sampling_rate": sampling_rate
                    },
                },
                {
                    "type": "text",
                    "text": "Please determine whether an Earthquake event has occurred in the provided time-series data. If so, please specify the starting time point indices of the P-wave and S-wave in the event."
                },
            ],
        }
    ]

    return client.chat.completions.create(
        model=model_name,
        messages=messages,
        temperature=0,
        max_tokens=200,
        extra_body={
            "chat_template_kwargs": {"enable_thinking": False}
        }
    )


def send_http_url(url: str, sampling_rate: int = 100):
    """http(s) url pointing to the time-series data."""
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "time_series_url",
                    "time_series_url": {
                        "url": url,
                        "sampling_rate": sampling_rate
                    },
                },
                {
                    "type": "text",
                    "text": "Please determine whether an Earthquake event has occurred in the provided time-series data. If so, please specify the starting time point indices of the P-wave and S-wave in the event."
                },
            ],
        }
    ]

    return client.chat.completions.create(
        model=model_name,
        messages=messages,
        temperature=0,
        max_tokens=200,
        extra_body={
            "chat_template_kwargs": {"enable_thinking": False}
        }
    )


def send_file_url(file_path: str, sampling_rate: int = 100):
    """file url pointing to the time-series data."""
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "time_series_url",
                    "time_series_url": {
                        "url": f"file://{file_path}",
                        "sampling_rate": sampling_rate
                    },
                },
                {
                    "type": "text",
                    "text": "Please determine whether an Earthquake event has occurred in the provided time-series data. If so, please specify the starting time point indices of the P-wave and S-wave in the event."
                },
            ],
        }
    ]

    return client.chat.completions.create(
        model=model_name,
        messages=messages,
        temperature=0,
        max_tokens=200,
        extra_body={
            "chat_template_kwargs": {"enable_thinking": False}
        }
    )

response = send_base64("./0092638_seism.npy")
# response = send_http_url("https://huggingface.co/internlm/Intern-S1-Pro/raw/main/0092638_seism.npy")
# response = send_file_url("./0092638_seism.npy")

print(response.choices[0].message)

For time series forecasting, forecast_horizon is optional. Set it to an integer to produce a forecast of exactly that length, or set it to None to let the model infer the horizon from the text prompt.

def forecast_base64(file_path: str, forecast_horizon: int | None = None):
    base64_ts = encode_time_series_base64(file_path)
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": (
                        "Please complete a electric load forecasting task. "
                        "This dataset is based on historical electricity load data every half hour within 24 hours of the region, "
                        "as well as data on minimum temperature, maximum temperature, humidity, air pressure, etc., "
                        "to predict future load consumption every half hour within 24 hours. Here is the weather information for city TAS: "
                        "Historical date weather: minimum temperature of 279.71K, maximum temperature of 285.83K, humidity of 85.0%, "
                        "air pressure of 1003.0hPa. Forecast date weather: minimum temperature 280.54K, maximum temperature 286.47K, "
                        "humidity 74.0%, air pressure 1007.0hPa. This data has no relevant effect information. "
                        "Please predict the next 48 time points given information above."
                    ),
                },
                {
                    "type": "time_series_url",
                    "time_series_url": {
                        "url": f"data:time_series/npy;base64,{base64_ts}",
                    },
                },
            ],
        }
    ]

    return client.chat.completions.create(
        model=model_name,
        messages=messages,
        temperature=0,
        max_tokens=16,
        extra_body={
            "chat_template_kwargs": {"enable_thinking": False},
            "enable_forecasting": True,
            "forecast_horizon": forecast_horizon,
        },
    )


response = forecast_base64("./load_20210803_0.npy", forecast_horizon=None)
forecast = response.choices[0].message.ts_forecast
print("Point forecast:", forecast.point_forecast)
print("Quantile forecast:", forecast.quantile_forecast)

Agent Integration

Intern-S2-397B can be plugged into agent frameworks in two ways: connecting to a self-hosted deployment, or calling the official InternLM API. Below we cover both, with examples for agent frameworks (OpenClaw, Hermes, etc.) and for Claude Code.

1. Self-hosted Deployment (LMDeploy as an example)

First, serve the model with LMDeploy following the Model Deployment Guide. The example below assumes the server is running at http://0.0.0.0:23333.

Connecting Agent Frameworks

Most agent frameworks (OpenClaw, Hermes, etc.) accept an OpenAI-compatible endpoint. Point them at the LMDeploy server base url http://0.0.0.0:23333/v1.

You can check the connection with the following command:

curl http://0.0.0.0:23333/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer EMPTY" \
  -d '{
    "model": "internlm/Intern-S2-397B",
    "messages": [
      {"role": "user", "content": "Hello"}
    ],
    "temperature": 0.8,
    "top_p": 0.95
  }'

Or you can configure your agent framework with the environment variables

export OPENAI_API_KEY=EMPTY
export OPENAI_BASE_URL=http://0.0.0.0:23333/v1
export OPENAI_MODEL=internlm/Intern-S2-397B

Remember to launch LMDeploy with --tool-call-parser interns2-preview so tool calls are parsed correctly.

Connecting Claude Code

LMDeploy exposes an Anthropic-compatible /v1/messages endpoint that Claude Code can talk to directly. Add the following to ~/.claude/settings.json:

{
  "env": {
    "ANTHROPIC_BASE_URL": "http://127.0.0.1:23333",
    "ANTHROPIC_AUTH_TOKEN": "dummy",
    "ANTHROPIC_MODEL": "internlm/Intern-S2-397B",
    "ANTHROPIC_CUSTOM_MODEL_OPTION": "internlm/Intern-S2-397B"
  }
}

For a full walkthrough (curl verification, model routing, troubleshooting), see LMDeploy × Claude Code.

2. Official Intern API

If you do not want to self-host, you can use the official Intern API. Register at internlm.intern-ai.org.cn and create an API token (sk-xxxxxxxx).

Connecting Agent Frameworks

The service is OpenAI-compatible, so any agent framework works. You can set the base url to https://chat.intern-ai.org.cn/api/v1 and the model name to intern-s2-397b in the cli or config file.

You can check the connection with the following command:

curl https://chat.intern-ai.org.cn/api/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxx" \
  -d '{
    "model": "intern-s2-397b",
    "messages": [
      {"role": "user", "content": "Hello"}
    ],
    "temperature": 0.8,
    "top_p": 0.95
  }'

Refer to the Intern API documentation for the current endpoint, available model names, rate limits, and advanced parameters.

Connecting Claude Code

Claude Code can route to the official Intern API by pointing ANTHROPIC_BASE_URL at the Intern Anthropic-compatible gateway:

{
  "env": {
    "ANTHROPIC_BASE_URL": "https://chat.intern-ai.org.cn",
    "ANTHROPIC_AUTH_TOKEN": "your-api-token",
    "ANTHROPIC_MODEL": "intern-s2-397b",
    "ANTHROPIC_SMALL_FAST_MODEL": "intern-s2-397b"
  }
}

Then start claude code with the following command:

claude --model intern-s2-397b

For step-by-step setup, see Intern API × Claude Code Integration.