Overview
Full fine-tuning involves retraining all parameters of a pre-trained language model on domain-specific data. This approach allows for maximum adaptation to specific tasks and domains.
Key Characteristics
Advantages
- Maximum adaptation to specific tasks
- Best performance for domain-specific tasks
- Complete control over model behavior
- Can incorporate domain-specific knowledge
Limitations
- High computational requirements
- Large memory footprint
- Risk of catastrophic forgetting
- Expensive to maintain multiple versions
Implementation Steps
- Prepare domain-specific training data
- Set up training infrastructure
- Configure hyperparameters
- Train the model
- Evaluate and validate
- Deploy the fine-tuned model
Best Practices
- Use high-quality, diverse training data
- Implement early stopping
- Monitor for overfitting
- Maintain validation sets
- Document training process