Fine-tune Llama 2 with DPO — TRL library simplifies RLHF

AI Impact Summary

Fine-tuning Llama 2 with DPO enables the creation of a more aligned and specialized language model by directly optimizing on preference data. This approach, utilizing the Direct Preference Optimization (DPO) method and the TRL library, simplifies the traditional Reinforcement Learning from Human Feedback (RLHF) pipeline by eliminating the need for a reward model and auxiliary training. The process involves supervised fine-tuning, followed by DPO training on a stack-exchange preference dataset, ultimately resulting in a model better aligned with human preferences.