When fine-tuning a Transformer-based model, such as BART, all parameters of the model are updated. This means, any tensor that is present in model.parameters() can have updated values after fine-tuning.
The configuration of a model (config.json) before and after fine-tuning can be identical. The configuration just defines basic hyperparameters such as the number of hidden layers, the number of attention heads, etc.