Parameter-Efficient Fine-Tuning in Machine Learning
In the rapidly evolving world of machine learning, fine-tuning pre-trained models to specific tasks has become a crucial process. However, traditional fine-tuning in machine learning can be computationally expensive and resource-intensive. This is where parameter-efficient fine-tuning (PEFT) steps in, offering a more efficient approach to adapting large pre-trained models to new tasks without incurring substantial…