Comprehensive Program for Overfitting Prevention and Control
The following combination of strategies is recommended for the overfitting phenomenon characteristic of large model fine-tuning:
- data enhancement: In preparation
.jsonWhen the dataset is expanded with data diversity through synonym replacement, sentence rewriting, etc., the data loader within the project supports automatic shuffling - regularization configuration: Add key parameters to the training script:
--weight_decay 0.01Control parameter update range--dropout 0.1Stochastic shielding of neurons
- Early Stop Mechanism: monitor the validation set loss and automatically stop it when there is no improvement for 3 consecutive rounds (built-in script)
EarlyStopping(Callbacks) - Courses of Study: Adjust the learning rate in stages, initially with
--lr 5e-5It drops to1e-6
An advanced solution could be to try the knowledge distillation feature provided by the project and constrain the student model with the output distribution of the teacher model.
This answer comes from the articleQwen3-FineTuning-Playground: a ready-to-use code base for fine-tuning Qwen3's big models.The
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