LLM Fine-Tuning

Best practice checklist for LLM fine-tuning:

1. Define clear objectives: Establish specific goals for fine-tuning, such as improving performance on particular tasks or domains.
2. Prepare high-quality data: Curate a diverse, representative, and clean dataset tailored to your specific use case.
3. Choose the right base model: Select an appropriate pre-trained model that aligns with your task and computational resources.
4. Determine the fine-tuning approach: Decide between full fine-tuning, parameter-efficient fine-tuning (e.g., LoRA, P-tuning), or prompt engineering based on your requirements and resources.
5. Set up proper evaluation metrics: Define relevant performance metrics and establish a robust evaluation framework.
6. Implement data augmentation: Use techniques like back-translation or paraphrasing to increase dataset diversity and prevent overfitting.
7. Apply regularization techniques: Implement methods like weight decay or dropout to prevent overfitting during fine-tuning.
8. Optimize hyperparameters: Conduct systematic hyperparameter tuning to find the optimal configuration for your specific task.
9. Monitor training progress: Regularly assess model performance during training to detect and address issues like overfitting or underfitting.
10. Validate on held-out data: Use a separate validation set to ensure the model generalizes well to unseen data.
11. Implement ethical considerations: Address potential biases and ensure responsible AI practices throughout the fine-tuning process.
12. Document the process: Maintain detailed records of your fine-tuning experiments, including data sources, model configurations, and results.
13. Iterate and refine: Continuously improve your model based on evaluation results and new insights.
14. Ensure scalability: Design your fine-tuning pipeline to accommodate future updates and larger datasets.
15. Implement version control: Use proper versioning for both data and model checkpoints to ensure reproducibility.