#Parameter-Efficient Fine-Tuning

Parameter-Efficient Fine-Tuning (PEFT) offers a cost-effective way to adapt large pretrained models with reduced computational and storage needs, maintaining high performance similar to fully fine-tuned models. PEFT works with tools like Transformers, Diffusers, and Accelerate, making it versatile for model training and inference across different domains. This method helps in managing large models on consumer hardware by minimizing memory consumption without compromising accuracy.

This project demonstrates the use of Proximal Policy Optimization (PPO) combined with Transformer Reinforcement Learning (TRL) and Low-Rank Adaptation (LoRA) for the fine-tuning of large language models. By leveraging Parameter-Efficient Fine-Tuning (PEFT), the project seeks to optimize models efficiently. It utilizes instruction data sourced from GPT-4-LLM to advance model training. The setup includes installing dependencies followed by a three-step process: supervised fine-tuning, reward model training, and PPO-based tuning. This structured approach supports comprehensive model optimization for advanced AI and machine learning applications.