Parameter Efficient Fine Tuning (PEFT)

A category encompassing techniques aimed at efficiently training large models, rather than denoting a specific technique itself. PEFT techniques optimize the fine-tuning process to achieve better efficiency while retaining model performance. Examples of techniques falling under the PEFT umbrella include LoRA and Adapters. These approaches streamline the training of large models by focusing on parameter efficiency, enabling faster convergence and improved resource utilization during fine-tuning tasks. By adopting PEFT strategies, practitioners can enhance the scalability and effectiveness of model training processes in resource-constrained environments.