original model and can be fine-tuned in a parameter-efficient way by tuning only their weights and leaving the rest of the model's weights frozen.

For some architectures, such as convolutional neural networks, it is common to keep the earlier layers (those closest to the input layer) frozen, as they capture lower-level features, while later layers often discern high-level features that can be more related to the task that the model