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# AstroSage-Llama-3.1-8B
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AstroSage-Llama-3.1-8B is a domain-specialized natural-language AI assistant tailored for research in astronomy, astrophysics, and cosmology. Trained on the complete collection of astronomy-related arXiv papers from 2007-2024 along with millions of synthetically-generated question-answer pairs and other astronomical literature, AstroSage-Llama-3.1-8B demonstrates excellent proficiency on a wide range of questions. This achievement demonstrates the potential of domain specialization in AI, suggesting that focused training can yield capabilities exceeding those of much larger, general-purpose models.
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## Model Details
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- Corresponding author: Tijmen de Haan (tijmen dot dehaan at gmail dot com)
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- AstroMLab: astromachinelearninglab at gmail dot com
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- Please cite the AstroMLab 3 paper when referencing this model
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# AstroSage-Llama-3.1-8B
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https://arxiv.org/abs/2411.09012
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AstroSage-Llama-3.1-8B is a domain-specialized natural-language AI assistant tailored for research in astronomy, astrophysics, and cosmology. Trained on the complete collection of astronomy-related arXiv papers from 2007-2024 along with millions of synthetically-generated question-answer pairs and other astronomical literature, AstroSage-Llama-3.1-8B demonstrates excellent proficiency on a wide range of questions. This achievement demonstrates the potential of domain specialization in AI, suggesting that focused training can yield capabilities exceeding those of much larger, general-purpose models.
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## Model Details
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- Corresponding author: Tijmen de Haan (tijmen dot dehaan at gmail dot com)
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- AstroMLab: astromachinelearninglab at gmail dot com
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- Please cite the AstroMLab 3 paper when referencing this model
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```
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@preprint{dehaan2024astromlab3,
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title={AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model},
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author={Tijmen de Haan and Yuan-Sen Ting and Tirthankar Ghosal and Tuan Dung Nguyen and Alberto Accomazzi and Azton Wells and Nesar Ramachandra and Rui Pan and Zechang Sun},
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year={2024},
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eprint={2411.09012},
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archivePrefix={arXiv},
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primaryClass={astro-ph.IM},
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url={https://arxiv.org/abs/2411.09012},
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}
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