In transfer learning, fine-tuning typically starts by taking a pre-trained LLM (like GPT) and continuing its training on a smaller, domain-specific dataset. The process involves:
Freezing early layers: To retain generalized knowledge.
Training later layers: To adjust to the new data.
Adjusting hyperparameters: Such as learning rate and batch size, to avoid overfitting. Fine-tuning allows the model to adapt to new tasks while leveraging its pre-existing language understanding.