Example Concept: Fine-tuning a transformer model typically involves adding a task-specific layer to the pre-trained model, such as a classification head for text classification tasks. The model is then trained on the new dataset using a smaller learning rate to adjust the weights slightly, preserving the pre-trained knowledge while learning the new task. This process requires a labeled dataset for the specific task and often involves techniques like early stopping to prevent overfitting.