What is Intern-S1
Intern-S1 is a scientific multimodal large model officially open-sourced and released by Shanghai Artificial Intelligence Laboratory at the World Artificial Intelligence Conference. It integrates language and multimodal capabilities, possesses a high level of balanced development ability, and is enriched with multidisciplinary professional knowledge, demonstrating outstanding performance in the scientific field. Intern-S1 pioneered the "Cross-modal Scientific Parsing Engine", which can accurately interpret various complex scientific modal data such as chemical molecular formulas, protein structures, and seismic wave signals. It can predict compound synthesis pathways, determine the feasibility of chemical reactions, and more. It surpasses top closed-source models on multidisciplinary professional task benchmarks, demonstrating exceptional scientific reasoning and understanding capabilities. Intern-S1 achieves deep integration of multiple scientific modalities through a dynamic Tokenizer and time-series signal encoder, adopts a general-purpose and specialized integrated scientific data synthesis method, and possesses powerful general reasoning capabilities and multiple top-tier professional capabilities.
Main Features of Intern-S1
- Cross-modal Scientific Parsing
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Chemistry: It can accurately interpret chemical molecular formulas, predict compound synthesis pathways, and determine the feasibility of chemical reactions.
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Biomedicine: It can parse protein sequences and assist in drug target discovery and clinical translation value assessment.
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Earth Science: It can identify seismic wave signals, analyze seismic wave events, and provide support for earthquake research.
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Language and Vision Fusion: It combines language and visual information to perform complex multimodal tasks, such as image-text question answering and scientific phenomenon explanation.
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Scientific Data Processing: It supports input of various complex scientific modal data, including light curves in materials science and gravitational wave signals in astronomy.
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Scientific Question Answering: Based on input scientific questions, combined with its powerful knowledge base and reasoning ability, it can provide accurate answers.
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Experimental Design and Optimization: It assists researchers in designing experimental plans, optimizing experimental processes, and improving research efficiency.
- Multi-agent Collaboration: It supports multi-agent systems and can collaborate with other agents to jointly complete complex scientific research tasks.
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Autonomous Learning and Evolution: It possesses a certain degree of autonomous learning ability and can continuously optimize its own performance through interaction with the environment.
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Data Processing and Analysis: It provides data processing and analysis tools to help researchers quickly process and analyze scientific data.
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Model Deployment and Application: It supports multiple deployment methods, including local deployment and cloud services, making it convenient for researchers to use in different scenarios.
Technical Principles of Intern-S1
- Innovative Multimodal Architecture: By adding a dynamic Tokenizer and time-series signal encoder, Intern-S1 supports various complex scientific modal data, including chemical molecular formulas, protein sequences, light curves, gravitational wave signals, and seismic waveforms. It innovatively achieves in-depth understanding and efficient processing of scientific modal data. For example, its compression rate for chemical molecular formulas is improved by more than 70% compared to DeepSeek-R1.
- Large-scale Scientific Domain Pre-training: The model is built on a 235-billion-parameter MoE language model and a 6-billion-parameter vision encoder, and has been pretrained on 5 trillion tokens of multimodal data, of which more than 2.5 trillion tokens come from the scientific domain. This enables the model to perform excellently in both general capabilities and specialized scientific domains, for example, demonstrating outstanding performance in specialized tasks such as chemical structure interpretation and protein sequence understanding.
- Jointly optimizing system and algorithm: The Intern-S1 R&D team achieved efficient and stable reinforcement learning training of a large multimodal MoE model under FP8 precision, reducing training cost by 10 times compared with recently publicly released MoE models. At the system level, it adopts an RL solution with separated training and inference, using a self-developed inference engine for efficient large-scale asynchronous inference under FP8; at the algorithm level, it proposes the Mixture of Rewards hybrid reward learning algorithm, integrating multiple rewards and feedback signals to improve training efficiency and stability.
- General-purpose and specialized integrated scientific data synthesis: To address the specialization needs of high-value tasks in the scientific domain, Intern-S1 adopts a general-purpose and specialized integrated scientific data synthesis method. On the one hand, it uses massive general scientific data to expand the model's knowledge scope; on the other hand, it uses specialized models to generate highly readable scientific data, with quality control performed by domain-customized specialized verification agents.
Intern-S1 project address
- Project official website：Intern Large Model
- Github repository：https://github.com/InternLM/Intern-S1
- HuggingFace model library：https://huggingface.co/internlm/Intern-S1-FP8
Application scenarios of Intern-S1
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Image and text fusion: Intern-S1 can handle image and text fusion tasks, such as describing the content in images and explaining scientific phenomena in images.
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Processing of complex scientific modal data: It supports input of various complex scientific modal data, including light curves in materials science and gravitational wave signals in astronomy, enabling deep fusion and efficient processing of these data.
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Integration with scientific research tools: Intern-S1 can be integrated into scientific research tools to help researchers quickly process and analyze scientific data.
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Answering scientific questions: As an intelligent assistant, Intern-S1 can answer various scientific questions based on its powerful knowledge base and reasoning capabilities.