Overfitting risk
When fine-tuning on small datasets, models tend to memorize training samples rather than learn generalizable patterns.
protective measure
- Utilize Unsloth's built-in normalization technologyConfigure weight_decay=0.01 in TrainingArguments.
- Set early stopping appropriatelyMonitoring the validation set loss to automatically stop training
- data enhancement: Utilizing Unsloth's long-text processing capabilities for paragraph restructuring
Tuning Recommendations
- Start with 3-5 epochs and gradually increase.
- Train multiple times using different random seeds and take the average.
- Finally, a comprehensive evaluation was conducted using Hugging Face Evaluate.
This answer comes from the articleUnsloth: an open source tool for efficiently fine-tuning and training large language modelsThe
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