In this guide, we walk through all the steps to fine-tune Meta's LLaMA model using Reinforcement Learning from Human Feedback (RLHF), creating the StackLLaMA model. By combining supervised fine-tuning, reward modeling, and RLHF, we produce a model that answers questions from the Stack Exchange platform with improved alignment.
The process starts with the LLaMA 7B model, a capable base that we then fine-tune. We use the Stack Exchange dataset, which provides questions and answers with upvotes and acceptance labels, as a proxy for human preferences. To assign reward scores, we follow the approach of Askell et al. 2021, converting upvotes to a logarithmic score.
Training a 7B model is memory-intensive, but we overcome this using Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA). Loading the model in 8-bit reduces memory to ~7 GB, and LoRA adds small adapter layers to train only a fraction of the parameters. This setup allows fine-tuning on a single 80GB A100 GPU.
The article covers supervised fine-tuning on high-scoring answers, training a reward model to predict human preferences, and then using proximal policy optimization (PPO) for the RLHF step. It also discusses challenges such as reward hacking and KL divergence issues, providing practical workarounds.
The final StackLLaMA model is available on the Hugging Face Hub, and the entire training pipeline is open-source in the TRL library. Try the demo to see the model in action!