Fine-tuning is the process of further training a pre-trained model on a specific dataset to adapt it for particular tasks or behaviors.
Aspect | Pre-training | Fine-tuning |
---|---|---|
Data volume | Massive (TB+) | Limited (MB-GB) |
Compute required | Enormous (months) | Moderate (hours-days) |
Cost | $1-100M+ | $10-10,000 |
Learning rate | Higher | Lower (to avoid catastrophic forgetting) |
Objective | General capabilities | Specific behaviors/knowledge |
Training on examples of desired inputs and outputs, typically created by human experts
Using human preferences to train a reward model that guides model optimization
Teaching models to follow natural language instructions in a consistent format
Tailoring a model to specific fields like medicine, law, or technical domains
Methods that update only a small subset of parameters, saving compute and memory
Create high-quality, task-specific training examples
Choose appropriate fine-tuning approach based on resources and goals
Set learning rate, batch size, epochs, etc. to optimize training
Update model weights on the training dataset
Test performance on validation data and adjust as needed
Implement the fine-tuned model in your application
Can achieve SOTA results on specific tasks
May forget general capabilities if not careful
Tailored to your specific requirements