Fine-tune Llama v2 7B with Direct Preference Optimization (DPO) via TRL

AI Impact Summary

Direct Preference Optimization (DPO) eliminates the reward-model/RLHF optimization step and trains directly on preference data, enabling a simpler fine-tuning workflow for Llama v2 7B. The approach relies on TRL's DPOTrainer with a base model and a reference model, requiring a dataset in the prompt/chosen/rejected format and leveraging 4-bit quantization and LoRA/PEFT tooling (bitsandbytes, AutoPeftModelForCausalLM). This reduces pipeline complexity and potential RL-related instability, but demands careful dataset construction and beta tuning (0.1–0.5) to control divergence from the reference and ensure meaningful optimization. Affected components include Llama v2 7B, the TRL library, DPOTrainer, AutoPeftModelForCausalLM, bitsandbytes, and the PEFT/LoRA tooling used for SFT and DPO training.