Offsite-Tuning: Transfer Learning without Full Model

Achieves comparable accuracy as full model fine-tuning while being privacy-preserving and efficient, gaining 6.5x speedup and 5.6x memory reduction.

Offsite-Tuning is a privacy-preserving and efficient transfer learning framework that can adapt billion-parameter foundation models to downstream data without access to the full model. It achieves comparable accuracy as full model fine-tuning while being computationally efficient and preserving both parties' privacy. This can benefit businesses that use foundation models for downstream tasks but can't fine-tune the models due to privacy concerns or computational limitations.