#supervised fine-tuning

This page details the methodologies used in training medical language models using GPT techniques, including pretraining, supervised fine-tuning, reinforcement learning from human feedback (RLHF), and direct preference optimization (DPO). By utilizing extensive multilingual datasets, MedicalGPT enhances performance in medical Q&A systems and supports various architectures such as Llama and Vicuna. The platform provides practical scripts and demo interfaces for ease of integration, serving as a significant resource for the development of contemporary medical AI applications.