Fine-tuning reduces the FM's size and complexity and enables slower inference.

Fine-tuning uses specific training data to retrain the FM from scratch to adapt to a specific use case.

Fine-tuning keeps the FM's knowledge up to date by pre-training the FM on more recent data.