Unified Efficient Fine-Tuning Techniques for Open-Source Large Language Models

Abstract Natural language processing has been revolutionized through the development of powerful large language models capable of understanding and generating human-like text, yet fine-tuning these models for specific tasks remains a computationally demanding process. Our novel approach to efficient fine-tuning, utilizing techniques such as adapter layers, Low-Rank Adaptation (LoRA), and layer-wise freezing, offers a significant reduction in computational overhead while maintaining or enhancing model performance. Extensive experiments with the Llama model demonstrated notable improvements in accuracy, F1-score, precision, and recall across multiple tasks, alongside marked reductions in GPU usage, training time, and memory consumption. The fine-tuned model also exhibited enhanced adaptability and generalization capabilities, performing well on new and unseen tasks, thus proving the efficacy and practical benefits of our methods. The contributions of this research provide valuable insights into optimizing large language models, broadening their applicability, and making advanced natural language processing techniques more accessible and efficient.