MixedPEFT combines invertible adapters and LoRA within a single framework to enable unsupervised domain adaptation. It simultaneously optimizes classification on source data and masked language modeling on target data, achieving 1.41% improvement over UDapter, 1.26% over DANN, and 0.86% over DSN using only 7% of the model's parameters.