Parameter Efficient Fine Tuning (PEFT) is a technique used to adapt models for specific tasks without retraining all model parameters. By modifying only a crucial subset of parameters, it reduces computational resource demands. This approach is particularly important for large models, where retraining the entire model is impractical. It's related to transfer learning, as both seek to apply existing models to new tasks but with varied efficiency in parameter adjustment.