Parameter-efficient fine-tuning refers to techniques in machine learning that allow for effective fine-tuning of models, especially large ones like transformers, without needing to adjust a large number of parameters.
The Concept Behind Parameter-Efficient Fine-Tuning
This approach is particularly important for adapting large pre-trained models to specific tasks or datasets while keeping computational and memory requirements manageable. Techniques include methods like adapters, where only a small part of the model is fine-tuned, or using regularization strategies to limit the degree of change to the model's parameters.
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