What is Fine-Tuning?
Fine-tuning is a transfer learning technique where a pre-trained model is further trained on a smaller, task-specific dataset. Instead of training from scratch, fine-tuning adapts the model's existing knowledge to perform well on a specific use case, requiring less data and compute than training from scratch.
How Fine-Tuning Works
- Start with a pre-trained base model
- Prepare task-specific training data
- Continue training on the new data
- Adjust hyperparameters as needed
- Evaluate on held-out test set
- Deploy the specialized model
Fine-Tuning Approaches
Full Fine-Tuning
- Update all model parameters
- Most flexible
- Requires more compute
Parameter-Efficient (PEFT)
- Update only some parameters
- LoRA, adapters, prefix tuning
- Less compute, smaller storage
Instruction Tuning
- Train on instruction-response pairs
- Improves task following
- Used for chat models
When to Fine-Tune
- Specialized domain knowledge needed
- Consistent output format required
- Performance on specific tasks is critical
- Prompt engineering is insufficient
Considerations
- Data quality is crucial
- Risk of catastrophic forgetting
- Ongoing maintenance required
- Cost-benefit analysis