This project focuses on enhancing open-source large language models through instruction-tuning and providing comprehensive evaluations of their performance. We explore how various training data factors, such as quantity, quality, and linguistic distribution, influence the performance of instruction-tuned models trained on publicly accessible high-quality instruction datasets for both English and Chinese languages. Our goal is to supplement evaluation with quantitative analyses, providing valuable insights for the continued advancement of open-source chat models. Our model, data, and code are publicly available for others to use and build upon.

翻译：本项目聚焦于通过指令微调增强开源大语言模型，并对其性能开展全面评估。我们探索了训练数据的数量、质量及语言分布等多维因素，如何影响基于公开高质量指令数据集（涵盖英文与中文）训练的指令微调模型的表现。旨在通过量化分析补充现有评估体系，为开源对话模型的持续进步提供重要见解。本研究的模型、数据及代码均已公开，可供他人使用和改进。