Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU

The article discusses the fine-tuning of 20 billion parameter large language models (LLMs) using Reinforcement Learning from Human Feedback (RLHF) on a consumer-grade GPU with 24GB of memory. It outlines the architectural adjustments made to accommodate the memory constraints, including gradient checkpointing and mixed-precision training. This approach enables practitioners to leverage powerful LLMs for specialized tasks without requiring extensive computational resources, democratizing access to advanced AI capabilities.