StackLLaMA: Train LLaMA with RLHF on Stack Exchange

AI Impact Summary

This guide details the training of a LLaMA model using Reinforcement Learning from Human Feedback (RLHF) on the Stack Exchange dataset. The process involves Supervised Fine-tuning (SFT), Reward/Preference Modeling, and RLHF, leveraging techniques like LoRA for efficient training and memory management. Specifically, the guide utilizes a 7B LLaMA model, employing 8-bit quantization and Parameter-Efficient Fine-Tuning (PEFT) with LoRA to reduce memory requirements and accelerate training on consumer hardware, demonstrating the feasibility of training large models on limited resources.