My personal thought is if your data is less, it will overfit quickly. If you want to avoid it, reduce epochs. But, best way is to gather more data.
Having said that neither transformer nor neural networks suffer too much from overfitting. Some papers are there I guess. It’s good in generalizing most of the times. Remember transformer like models have quite good number of parameters, that’s also one reason of overfitting. But in downstream tasks, even if it overfits, it’s useful right.
Pre training is like a person who graduate with Masters. Fine tuning is like doing PhD ( except here it is quick  , use your graduation skills to be an expert in specific field. So overfitting is okay. Personal opinion only.