Fine-tuning
Re-training a pre-trained model for specific tasks
Fine-tuning is a training method that adjusts a pre-trained model with general knowledge for specific purposes. There is Full Fine-tuning (updating all weights) and partial methods (updating only some layers). The core principle is Transfer Learning โ maintaining language understanding from pre-training while learning new domain patterns. For example, fine-tuning on medical papers creates a medical specialist model, and on legal documents creates a legal specialist. Setting the learning rate much lower than pre-training is crucial to preserve existing knowledge.
Key Concepts
Select a Pre-trained Base Model (GPT, LLaMA, Mistral, etc.)
Prepare task-specific training data (input-output pairs, thousands to tens of thousands)
Set a low learning rate (1e-5 ~ 5e-5, 10-100x smaller than pre-training)
Update weights with task data โ learn new patterns while preserving existing knowledge
Check for overfitting with validation data (Early Stopping)
Verify performance with evaluation metrics (accuracy, F1, BLEU, etc.)
Pros
- ✓ Specialization with far less data/cost compared to pre-training
- ✓ Dramatically improved domain-specific performance
- ✓ Existing general knowledge is preserved
- ✓ Can leverage open-source Base Models
Cons
- ✗ Requires high-quality training data
- ✗ Catastrophic Forgetting โ risk of degrading existing capabilities
- ✗ Requires hyperparameter tuning
- ✗ Full Fine-tuning requires significant GPU memory