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Parameter-efficient Fine-tuning (or “PEFT”)
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.
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