Fine-Tuning Llama-3 With Lora: A Memory-Efficient Approach to Optimizing Generative AI Models

Large language models like LLaMA-3 have demonstrated significant potential in generating contextually relevant and coherent text for various applications.However, fine-tuning these models is resource-intensive, requiring substantial memory and computational power.This paper explores the use of Low-Rank Adaptation (LoRA) as a method to efficiently fine-tune LLaMA-3 while minimizing memory usage.By leveraging 4-bit quantization and LoRA configurations, we demonstrate an optimized approach to fine-tuning that retains model performance.Our experiments show that this method enables the generation of high-quality content with reduced computational overhead.