Fine-tuning LLama 2 with PEFT, LoRA, 4-bit Quantization, TRL and SFT

Learn how to fine-tune the LLama 2 model in this 15-minute technical tutorial that demonstrates parameter efficient fine-tuning techniques, including low rank approximation of matrix and tensor structures, 4-bit quantization of tensors, transformer-based Reinforcement Learning (RL), and HuggingFace's Supervised Fine-tuning trainer. Create synthetic datasets using GPT-4 or CLAUDE 2 as the central intelligence to generate task-specific training data for fine-tuning Large Language Models based on user queries. Follow along with code examples based on Matt Shumer's Jupyter Notebook implementation for customizing and optimizing LLama 2's performance.