HomeAliyun's Open-Source Scientific Model LOGOS Beats Microsoft with 1/56th the Parameters
Alibaba's ATH-Token Foundry, in collaboration with the Gaoqiang Institute of Artificial Intelligence at Renmin University of China, has announced the open-source release of LOGOS. This is the first multi-domain scientific generative foundation model built on a unified scientific syntax, consistently matching or surpassing traditional domain-specific methods across six representative scientific tasks using pure sequence modeling.
What stands out is the model's exceptional parameter efficiency. With just 1 billion parameters, LOGOS-1B outperforms Microsoft's NatureLM (which has 8×7B parameters) on several key tasks.
Pioneering Unified Scientific Syntax for Heterogeneous Objects
LOGOS uses a large pre-training corpus covering seven modalities—biological macromolecules, chemical entities, and interface interactions—amounting to 44.87 billion tokens. Through a shared vocabulary, it encodes formerly heterogeneous objects like proteins and small molecules into a unified discrete token sequence.
This distinctive scientific syntax allows large models to autoregressively interpret different scientific objects within a shared generation space. It even introduces a "text description method" that lets complex spatial interaction rules be conceptualized without inputting intricate 3D coordinates, relying purely on sequence prediction.
Bridging the Gap Between Pre-training and Application
In conventional research settings, switching models between stages often requires extensive fine-tuning at deployment. LOGOS achieves strong consistency in both form and purpose, as its pre-training data sequence format matches the input and output formats of downstream tasks.
This alignment effectively removes the divide between pre-training and real-world applications, enabling direct generative capabilities without complex adaptation layers. Alibaba has now fully open-sourced the model weights, inference code, and technical report for this large model.
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Alibaba's ATH-Token Foundry, in collaboration with the Gaoqiang Institute of Artificial Intelligence at Renmin University of China, has announced the open-source release of LOGOS. This is the first multi-domain scientific generative foundation model built on a unified scientific syntax, consistently matching or surpassing traditional domain-specific methods across six representative scientific tasks using pure sequence modeling.
What stands out is the model's exceptional parameter efficiency. With just 1 billion parameters, LOGOS-1B outperforms Microsoft's NatureLM (which has 8×7B parameters) on several key tasks.
Pioneering Unified Scientific Syntax for Heterogeneous Objects
LOGOS uses a large pre-training corpus covering seven modalities—biological macromolecules, chemical entities, and interface interactions—amounting to 44.87 billion tokens. Through a shared vocabulary, it encodes formerly heterogeneous objects like proteins and small molecules into a unified discrete token sequence.
This distinctive scientific syntax allows large models to autoregressively interpret different scientific objects within a shared generation space. It even introduces a "text description method" that lets complex spatial interaction rules be conceptualized without inputting intricate 3D coordinates, relying purely on sequence prediction.
Bridging the Gap Between Pre-training and Application
In conventional research settings, switching models between stages often requires extensive fine-tuning at deployment. LOGOS achieves strong consistency in both form and purpose, as its pre-training data sequence format matches the input and output formats of downstream tasks.
This alignment effectively removes the divide between pre-training and real-world applications, enabling direct generative capabilities without complex adaptation layers. Alibaba has now fully open-sourced the model weights, inference code, and technical report for this large model.
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