Hanminwang Hanminwang
2024
MIMIR: A Customizable Agent Tuning Platform for Enhanced Scientific Applications
Xiangru Tang
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Chunyuan Deng
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Hanminwang Hanminwang
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Haoran Wang
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Yilun Zhao
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Wenqi Shi
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Yi Fung
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Wangchunshu Zhou
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Jiannan Cao
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Heng Ji
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Arman Cohan
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Mark Gerstein
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Recently, large language models (LLMs) have evolved into interactive agents, proficient in planning, tool use, and task execution across various tasks. However, without agent-tuning, open-source models like LLaMA2 currently struggle to match the efficiency of larger models such as GPT-4 in scientific applications due to a lack of agent tuning datasets. In response, we introduce MIMIR, a streamlined platform that leverages large LLMs to generate agent-tuning data for fine-tuning smaller, specialized models. By employing a role-playing methodology, MIMIR enables larger models to simulate various roles and create interaction data, which can then be used to fine-tune open-source models like LLaMA2. This approach ensures that even smaller models can effectively serve as agents in scientific tasks. Integrating these features into an end-to-end platform, MIMIR facilitates everything from the uploading of scientific data to one-click agent fine-tuning. MIMIR is publicly released and actively maintained at https://github. com/gersteinlab/MIMIR, along with a demo video for quick-start, calling for broader development.
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Co-authors
- Xiangru Tang 1
- Chunyuan Deng 1
- Haoran Wang 1
- Yilun Zhao 1
- Wenqi Shi 1
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