Quokka: An Open-source Large Language Model ChatBot for Material Science
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content
Specification-based Security Evaluation of Real-World Agents Tool-Calling under Long-Horizon Context Tasks
Assay2Mol: large language model-based drug design using BioAssay context
Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery
Detecting Referring Expressions in Visually Grounded Dialogue with Autoregressive Language Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.