Intern-S1: A Scientific Multimodal Foundation Model
Authors: name: Lei Bai, name: Zhongrui Cai, name: Maosong Cao, name: Weihan Cao, name: Chiyu Chen, name: Haojiong Chen, name: Kai Chen, name: Pengcheng Chen, name: Ying Chen, name: Yongkang Chen, name: Yu Cheng, name: Yu Cheng, name: Pei Chu, name: Tao Chu, name: Erfei Cui, name: Ganqu Cui, name: Long Cui, name: Ziyun Cui, name: Nianchen Deng, name: Ning Ding, name: Nanqin Dong, name: Peijie Dong, name: Shihan Dou, name: Sinan Du, name: Haodong Duan, name: Caihua Fan, name: Ben Gao, name: Changjiang Gao, name: Jianfei Gao, name: Songyang Gao, name: Yang Gao, name: Zhangwei Gao, name: Jiaye Ge, name: Qiming Ge, name: Lixin Gu, name: Yuzhe Gu, name: Aijia Guo, name: Qipeng Guo, name: Xu Guo, name: Conghui He, name: Junjun He, name: Yili Hong, name: Siyuan Hou, name: Caiyu Hu, name: Hanglei Hu, name: Jucheng Hu, name: Ming Hu, name: Zhouqi Hua, name: Haian Huang, name: Junhao Huang, name: Xu Huang, name: Zixian Huang, name: Zhe Jiang, name: Lingkai Kong, name: Linyang Li, name: Peiji Li, name: Pengze Li, name: Shuaibin Li, name: Tianbin Li, name: Wei Li, name: Yuqiang Li, name: Dahua Lin, name: Junyao Lin, name: Tianyi Lin, name: Zhishan Lin, name: Hongwei Liu, name: Jiangning Liu, name: Jiyao Liu, name: Junnan Liu, name: Kai Liu, name: Kaiwen Liu, name: Kuikun Liu, name: Shichun Liu, name: Shudong Liu, name: Wei Liu, name: Xinyao Liu, name: Yuhong Liu, name: Zhan Liu, name: Yinquan Lu, name: Haijun Lv, name: Hongxia Lv, name: Huijie Lv, name: Qidang Lv, name: Ying Lv, name: Chengqi Lyu, name: Chenglong Ma, name: Jianpeng Ma, name: Ren Ma, name: Runmin Ma, name: Runyuan Ma, name: Xinzhu Ma, name: Yichuan Ma, name: Zihan Ma, name: Sixuan Mi, name: Junzhi Ning, name: Wenchang Ning, name: Xinle Pang, name: Jiahui Peng, name: Runyu Peng, name: Yu Qiao, name: Jiantao Qiu, name: Xiaoye Qu, name: Yuan Qu, name: Yuchen Ren, name: Fukai Shang, name: Wenqi Shao, name: Junhao Shen, name: Shuaike Shen, name: Chunfeng Song, name: Demin Song, name: Diping Song, name: Chenlin Su, name: Weijie Su, name: Weigao Sun, name: Yu Sun, name: Qian Tan, name: Cheng Tang, name: Huanze Tang, name: Kexian Tang, name: Shixiang Tang, name: Jian Tong, name: Aoran Wang, name: Bin Wang, name: Dong Wang, name: Lintao Wang, name: Rui Wang, name: Weiyun Wang, name: Wenhai Wang, name: Yi Wang, name: Ziyi Wang, name: Ling-I Wu, name: Wen Wu, name: Yue Wu, name: Zijian Wu, name: Linchen Xiao, name: Shuhao Xing, name: Chao Xu, name: Huihui Xu, name: Jun Xu, name: Ruiliang Xu, name: Wanghan Xu, name: GanLin Yang, name: Yuming Yang, name: Haochen Ye, name: Jin Ye, name: Shenglong Ye, name: Jia Yu, name: Jiashuo Yu, name: Jing Yu, name: Fei Yuan, name: Bo Zhang, name: Chao Zhang, name: Chen Zhang, name: Hongjie Zhang, name: Jin Zhang, name: Qiaosheng Zhang, name: Qiuyinzhe Zhang, name: Songyang Zhang, name: Taolin Zhang, name: Wenlong Zhang, name: Wenwei Zhang, name: Yechen Zhang, name: Ziyang Zhang, name: Haiteng Zhao, name: Qian Zhao, name: Xiangyu Zhao, name: Xiangyu Zhao, name: Bowen Zhou, name: Dongzhan Zhou, name: Peiheng Zhou, name: Yuhao Zhou, name: Yunhua Zhou, name: Dongsheng Zhu, name: Lin Zhu, name: Yicheng Zou

In recent years, a plethora of open-source foundation models have emerged,
achieving remarkable progress in some widely attended fields, with performance
being quite close to that of closed-source models. However, in high-value but
more challenging scientific professional fields, either the fields still rely
on expert models, or the progress of general foundation models lags
significantly compared to those in popular areas, far from sufficient for
transforming scientific research and leaving substantial gap between
open-source models and closed-source models in these scientific domains. To
mitigate this gap and explore a step further toward Artificial General
Intelligence (AGI), we introduce Intern-S1, a specialized generalist equipped
with general understanding and reasoning capabilities with expertise to analyze
multiple science modal data. Intern-S1 is a multimodal Mixture-of-Experts (MoE)
model with 28 billion activated parameters and 241 billion total parameters,
continually pre-trained on 5T tokens, including over 2.5T tokens from
scientific domains. In the post-training stage, Intern-S1 undergoes offline and
then online reinforcement learning (RL) in InternBootCamp, where we propose
Mixture-of-Rewards (MoR) to synergize the RL training on more than 1000 tasks
simultaneously. Through integrated innovations in algorithms, data, and
training systems, Intern-S1 achieved top-tier performance in online RL
training.On comprehensive evaluation benchmarks, Intern-S1 demonstrates
competitive performance on general reasoning tasks among open-source models and
significantly outperforms open-source models in scientific domains, surpassing
closed-source state-of-the-art models in professional tasks, such as molecular
synthesis planning, reaction condition prediction, predicting thermodynamic
stabilities for crystals. Our models are available at
https://huggingface.co/internlm/Intern-S1.