Parameter-efficient Fine-tuning (PEFT) is a technique that focuses on optimizing and adapting a pre-trained model’s parameters to new tasks with minimal additional training data. It tries to improve model performance while minimizing the need for lengthy retraining and lowering the risk of overfitting.
Parameter-efficient Fine-tuning (or “PEFT”)
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