LoRA vs. Full Fine-Tuning: A Comparative Study
“How good is LoRA for fine-tuning LLMs really? 👀” The new study “LoRA Learns Less and Forgets Less” delves into the performance differences between LoRA and full fine-tuning across programming and mathematics domains. LoRA and Q-LoRA, known for their parameter-efficient methods, save memory by training only select layers of models. The study conducted several experiments: Training Llama-2 7B and 13B models on programming and mathematics datasets. Exploring instruction tuning and continued pretraining. Comparing LoRA and full fine-tuning across various configurations, including target modules, rank, learning rates, and number of epochs. Evaluating performance on code tasks (HumanEval) and math tasks (GSM8K). Key insights include: LoRA maintains more diverse generations. Acts as a regularizer, preventing overfitting, which might explain DPO's effectiveness. Underperforms compared to full fine-tuning in the target domain. Better retains the base model’s performance on tasks outside the target domain. Full fine-tuning proves more accurate and sample-efficient in both code and math. The choice of target modules is more critical than the rank (r). This comprehensive comparison highlights the strengths and weaknesses of each method, providing valuable insights for developers optimizing large language models for specific tasks.