  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.
