  Chat models, such as ChatGPT, have shown impressive capabilities and have
been rapidly adopted across numerous domains. However, these models are only
accessible through a restricted API, creating barriers for new research and
progress in the field. We propose a pipeline that can automatically generate a
high-quality multi-turn chat corpus by leveraging ChatGPT to engage in a
conversation with itself. Subsequently, we employ parameter-efficient tuning to
enhance LLaMA, an open-source large language model. The resulting model, named
Baize, demonstrates good performance in multi-turn dialogues with guardrails
that minimize potential risks. Furthermore, we propose a new technique called
Self-Distill with Feedback, to further improve the performance of the Baize
models with feedback from ChatGPT. The Baize models and data are released for
research purposes only at https://github.com/project-baize/baize-chatbot. An
online demo is also available at
https://huggingface.co/spaces/project-baize/chat-with-baize.
