Participants can implement K-fold cross-validation to train the model on different data splits. This approach ensures that LoRA fine-tuning is robust and evaluated across various subsets of the data, leading to more reliable performance metrics.
Please ensure that you've read the guidelines present in CONTRIBUTING.md as well as the CODE_OF_CONDUCT.md.
Participants can implement K-fold cross-validation to train the model on different data splits. This approach ensures that LoRA fine-tuning is robust and evaluated across various subsets of the data, leading to more reliable performance metrics.
Please ensure that you've read the guidelines present in CONTRIBUTING.md as well as the CODE_OF_CONDUCT.md.