INTERN-S1: A Scientific Multimodal Foundation Model скачать в хорошем качестве

INTERN-S1: A Scientific Multimodal Foundation Model

Intern-S1 is a specialized artificial intelligence model designed to bridge the significant performance gap between open-source and closed-source systems in complex scientific fields such as chemistry, physics, and materials science. Built on a multimodal Mixture-of-Experts architecture, the model integrates specific encoders for vision and time-series data, along with a dynamic tokenizer that efficiently processes scientific notations like molecular structures and protein sequences. Its development involved pre-training on a massive dataset of 5 trillion tokens, including over 2.5 trillion tokens dedicated to scientific knowledge acquired through advanced data parsing pipelines. To refine its reasoning capabilities, the researchers employed a novel reinforcement learning strategy known as Mixture-of-Rewards, which harmonizes feedback from over 1,000 different tasks to train the model effectively across diverse scenarios. This rigorous training regimen has allowed Intern-S1 to achieve state-of-the-art performance among open-source models, often surpassing leading proprietary models in challenging tasks like predicting chemical reaction conditions and molecular synthesis planning. https://arxiv.org/pdf/2508.15763 https://huggingface.co/internlm/Inter...