我们推出Intern-S1-Pro，这是一款万亿级混合专家（MoE）多模态科学推理模型。Intern-S1-Pro 规模达到 1 万亿总参数，包含 512 个专家，每个 token 激活 8 个专家（激活参数为 220 亿）。该模型在高级推理基准测试中展现出顶尖性能，并在关键 AI4Science 领域（化学、材料、生命科学、地球科学等）取得领先成果，同时保持强大的通用多模态和文本处理能力。
我们在多种基准测试上对 Intern-S1-Pro 进行了评估，包括通用数据集和科学数据集。以下报告了该模型与近期视觉语言模型（VLM）和大型语言模型（LLM）的性能对比。
注：下划线表示开源模型中的最佳性能，粗体表示所有模型中的最佳性能。
我们使用 OpenCompass 和 VLMEvalKit 对所有模型进行评估。
我们建议使用以下超参数以确保获得更优结果
top_p = 0.95
top_k = 50
min_p = 0.0
temperature = 0.8[!IMPORTANT] 使用原生 Hugging Face 的前向传播方法运行万亿参数模型具有挑战性。我们强烈建议使用 LLM 推理引擎（如 LMDeploy、vLLM 或 sglang）来托管 Intern-S1-Pro，并通过 API 访问模型。
Intern-S1-Pro 可通过以下任一 LLM 推理框架进行部署：
这些框架的详细部署示例可在 模型部署指南 中找到。
时序模块的部署支持正在优化中，即将发布。
如今，许多大型语言模型（LLM）都具备工具调用功能，这是一项强大的能力，允许模型通过与外部工具和 API 交互来扩展其功能。这使模型能够执行诸如获取最新信息、运行代码或调用其他应用程序内函数等任务。
对开发人员而言，一个重要优势是越来越多的开源 LLM 被设计为与 OpenAI API 兼容。这意味着您可以利用 OpenAI 库中相同的熟悉语法和结构，在这些开源模型上实现工具调用。因此，本教程中展示的代码具有通用性——不仅适用于 OpenAI 模型，还适用于任何遵循相同接口标准的模型。
为了说明其工作原理，让我们深入了解一个实用的代码示例，该示例使用工具调用获取最新天气预报（基于 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 = client.models.list().data[0].id
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)Intern-S1-Pro 默认启用思考模式，以增强模型的推理能力，从而生成更高质量的响应。可通过在 tokenizer.apply_chat_template 中设置 enable_thinking=False 来禁用此功能。
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # think mode indicator
)通过部署 Intern-S1-Pro 模型，您可以在请求中调整 enable_thinking 参数来动态控制思考模式。
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 = client.models.list().data[0].id
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))如果您觉得本研究工作对您有所帮助，欢迎引用我们的成果。
@misc{bai2025interns1scientificmultimodalfoundation,
title={Intern-S1: A Scientific Multimodal Foundation Model},
author={Lei Bai and Zhongrui Cai and Maosong Cao and Weihan Cao and Chiyu Chen and Haojiong Chen and Kai Chen and Pengcheng Chen and Ying Chen and Yongkang Chen and Yu Cheng and Yu Cheng and Pei Chu and Tao Chu and Erfei Cui and Ganqu Cui and Long Cui and Ziyun Cui and Nianchen Deng and Ning Ding and Nanqin Dong and Peijie Dong and Shihan Dou and Sinan Du and Haodong Duan and Caihua Fan and Ben Gao and Changjiang Gao and Jianfei Gao and Songyang Gao and Yang Gao and Zhangwei Gao and Jiaye Ge and Qiming Ge and Lixin Gu and Yuzhe Gu and Aijia Guo and Qipeng Guo and Xu Guo and Conghui He and Junjun He and Yili Hong and Siyuan Hou and Caiyu Hu and Hanglei Hu and Jucheng Hu and Ming Hu and Zhouqi Hua and Haian Huang and Junhao Huang and Xu Huang and Zixian Huang and Zhe Jiang and Lingkai Kong and Linyang Li and Peiji Li and Pengze Li and Shuaibin Li and Tianbin Li and Wei Li and Yuqiang Li and Dahua Lin and Junyao Lin and Tianyi Lin and Zhishan Lin and Hongwei Liu and Jiangning Liu and Jiyao Liu and Junnan Liu and Kai Liu and Kaiwen Liu and Kuikun Liu and Shichun Liu and Shudong Liu and Wei Liu and Xinyao Liu and Yuhong Liu and Zhan Liu and Yinquan Lu and Haijun Lv and Hongxia Lv and Huijie Lv and Qidang Lv and Ying Lv and Chengqi Lyu and Chenglong Ma and Jianpeng Ma and Ren Ma and Runmin Ma and Runyuan Ma and Xinzhu Ma and Yichuan Ma and Zihan Ma and Sixuan Mi and Junzhi Ning and Wenchang Ning and Xinle Pang and Jiahui Peng and Runyu Peng and Yu Qiao and Jiantao Qiu and Xiaoye Qu and Yuan Qu and Yuchen Ren and Fukai Shang and Wenqi Shao and Junhao Shen and Shuaike Shen and Chunfeng Song and Demin Song and Diping Song and Chenlin Su and Weijie Su and Weigao Sun and Yu Sun and Qian Tan and Cheng Tang and Huanze Tang and Kexian Tang and Shixiang Tang and Jian Tong and Aoran Wang and Bin Wang and Dong Wang and Lintao Wang and Rui Wang and Weiyun Wang and Wenhai Wang and Yi Wang and Ziyi Wang and Ling-I Wu and Wen Wu and Yue Wu and Zijian Wu and Linchen Xiao and Shuhao Xing and Chao Xu and Huihui Xu and Jun Xu and Ruiliang Xu and Wanghan Xu and GanLin Yang and Yuming Yang and Haochen Ye and Jin Ye and Shenglong Ye and Jia Yu and Jiashuo Yu and Jing Yu and Fei Yuan and Bo Zhang and Chao Zhang and Chen Zhang and Hongjie Zhang and Jin Zhang and Qiaosheng Zhang and Qiuyinzhe Zhang and Songyang Zhang and Taolin Zhang and Wenlong Zhang and Wenwei Zhang and Yechen Zhang and Ziyang Zhang and Haiteng Zhao and Qian Zhao and Xiangyu Zhao and Xiangyu Zhao and Bowen Zhou and Dongzhan Zhou and Peiheng Zhou and Yuhao Zhou and Yunhua Zhou and Dongsheng Zhu and Lin Zhu and Yicheng Zou},
year={2025},
eprint={2508.15763},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.15763},
}@misc{zou2026interns1proscientificmultimodalfoundation,
title={Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale},
author={Yicheng Zou and Dongsheng Zhu and Lin Zhu and Tong Zhu and Yunhua Zhou and Peiheng Zhou and Xinyu Zhou and Dongzhan Zhou and Zhiwang Zhou and Yuhao Zhou and Bowen Zhou and Zhanping Zhong and Zhijie Zhong and Haiteng Zhao and Penghao Zhao and Xiaomeng Zhao and Zhiyuan Zhao and Yechen Zhang and Jin Zhang and Wenwei Zhang and Hongjie Zhang and Zhuo Zhang and Wenlong Zhang and Bo Zhang and Chao Zhang and Chen Zhang and Yuhang Zang and Fei Yuan and Jiakang Yuan and Jiashuo Yu and Jinhui Yin and Haochen Ye and Qian Yao and Bowen Yang and Danni Yang and Kaichen Yang and Ziang Yan and Jun Xu and Yicheng Xu and Wanghan Xu and Xuenan Xu and Chao Xu and Ruiliang Xu and Shuhao Xing and Long Xing and Xinchen Xie and Ling-I Wu and Zijian Wu and Zhenyu Wu and Lijun Wu and Yue Wu and Jianyu Wu and Wen Wu and Fan Wu and Xilin Wei and Qi Wei and Bingli Wang and Rui Wang and Ziyi Wang and Zun Wang and Yi Wang and Haomin Wang and Yizhou Wang and Lintao Wang and Yiheng Wang and Longjiang Wang and Bin Wang and Jian Tong and Zhongbo Tian and Huanze Tang and Chen Tang and Shixiang Tang and Yu Sun and Qiushi Sun and Xuerui Su and Qisheng Su and Chenlin Su and Demin Song and Jin Shi and Fukai Shang and Yuchen Ren and Pengli Ren and Xiaoye Qu and Yuan Qu and Jiantao Qiu and Yu Qiao and Runyu Peng and Tianshuo Peng and Jiahui Peng and Qizhi Pei and Zhuoshi Pan and Linke Ouyang and Wenchang Ning and Yichuan Ma and Zerun Ma and Ningsheng Ma and Runyuan Ma and Chengqi Lyu and Haijun Lv and Han Lv and Lindong Lu and Kuikun Liu and Jiangning Liu and Yuhong Liu and Kai Liu and Hongwei Liu and Zhoumianze Liu and Mengjie Liu and Ziyu Liu and Wenran Liu and Yang Liu and Liwei Liu and Kaiwen Liu and Junyao Lin and Junming Lin and Tianyang Lin and Dahua Lin and Jianze Liang and Linyang Li and Peiji Li and Zonglin Li and Zehao Li and Pengze Li and Guoyan Li and Lingkai Kong and Linglin Jing and Zhenjiang Jin and Feifei Jiang and Qian Jiang and Junhao Huang and Zixian Huang and Haian Huang and Zhouqi Hua and Han Hu and Linfeng Hou and Yinan He and Conghui He and Tianyao He and Xu Guo and Qipeng Guo and Aijia Guo and Yuzhe Gu and Lixin Gu and Jingyang Gong and Qiming Ge and Jiaye Ge and Songyang Gao and Jianfei Gao and Xinyu Fang and Caihua fan and Yue Fan and Yanhui Duan and Zichen Ding and Shengyuan Ding and Xuanlang Dai and Erfei Cui and Ganqu Cui and Pei Chu and Tao Chu and Guangran Cheng and Yu Cheng and Kai Chen and Yongkang Chen and Chiyu Chen and Guanzhou Chen and Qiaosheng Chen and Sitao Chen and Xin Chen and Haojiong Chen and Yicheng Chen and Weihan Cao and Yuhang Cao and Qinglong Cao and Lei Bai},
year={2026},
eprint={2603.25040},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2603.25040},
}