Panda LLM: Training Data and Evaluation for Open-Sourced Chinese Instruction-Following Large Language Models
Fangkai Jiao, Bosheng Ding, Tianze Luo, Zhanfeng Mo
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.