This paper presents the development of a specialized chatbot for materials science, leveraging the Llama-2 language model, and continuing pre-training on the expansive research articles in the materials science domain from the S2ORC dataset. The methodology involves an initial pretraining phase on over one million domain-specific papers, followed by an instruction-tuning process to refine the chatbot's capabilities. The chatbot is designed to assist researchers, educators, and students by providing instant, context-aware responses to queries in the field of materials science. We make the four trained checkpoints (7B, 13B, with or without chat ability) freely available to the research community at https://github.com/Xianjun-Yang/Quokka.
@article{arxiv.2401.01089,
title = {Quokka: An Open-source Large Language Model ChatBot for Material Science},
author = {Xianjun Yang and Stephen D. Wilson and Linda Petzold},
journal= {arXiv preprint arXiv:2401.01089},
year = {2024}
}