General large language models (LLMs) such as ChatGPT have shown remarkable
success. However, such LLMs have not been widely adopted for medical purposes,
due to poor accuracy and inability to provide medical advice. We propose
IvyGPT, an LLM based on LLaMA that is trained and fine-tuned with high-quality
medical question-answer (QA) instances and Reinforcement Learning from Human
Feedback (RLHF). After supervised fine-tuning, IvyGPT has good multi-turn
conversation capabilities, but it cannot perform like a doctor in other
aspects, such as comprehensive diagnosis. Through RLHF, IvyGPT can output
richer diagnosis and treatment answers that are closer to human. In the
training, we used QLoRA to train 33 billion parameters on a small number of
NVIDIA A100 (80GB) GPUs. Experimental results show that IvyGPT has outperformed
other medical GPT models.

通过高质量的医学问答实例和人类反馈进行训练和微调的基于 LaMA 的 IvyGPT，具有较好的多轮对话能力，并能输出接近人类的丰富诊断和治疗答案，超过其他医学 GPT 模型。