Intern-S1: A Scientific Multimodal Foundation Model
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.
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Intern-S1: A Scientific Multimodal Foundation Model
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.
Intern-S1, a multimodal Mixture-of-Experts model with extensive pre-training and reinforcement learning, achieves top-tier performance in general reasoning and outperforms closed-source models in scientific tasks.
Mixture-of-Experts (MoE) reinforcement learning (RL) Mixture-of-Rewards (MoR) molecular synthesis planning reaction condition prediction thermodynamic stabilities