Participants can implement a cyclic learning rate scheduler, which alternates between low and high learning rates during training. This technique can help LoRA layers escape local minima, improve convergence, and reduce overfitting.
Ensure that you've read the guidelines present in CONTRIBUTING.md as well as the CODE_OF_CONDUCT.md.
Participants can implement a cyclic learning rate scheduler, which alternates between low and high learning rates during training. This technique can help LoRA layers escape local minima, improve convergence, and reduce overfitting.
Ensure that you've read the guidelines present in CONTRIBUTING.md as well as the CODE_OF_CONDUCT.md.