Fine-tuning 20B LLMs with RLHF on a 24GB GPU — PEFT & 8bit-Matrix-Multiplication

AI Impact Summary

This documentation details the integration of the trl library with peft to enable efficient RLHF fine-tuning of 20B LLMs on consumer GPUs. The key challenge highlighted is the memory requirements of these large models, particularly when using full-precision training (float32), which can necessitate 40GB of GPU memory just to fit the model. The solution involves leveraging techniques like 8-bit matrix multiplication and low-rank adaptation (LoRA) via PEFT to significantly reduce the memory footprint, allowing training on a 24GB GPU.