Offsite-Tuning: Transfer Learning without Full Model

Guangxuan Xiao¹, Ji Lin¹, Song Han¹
Massachusetts Institute of Technology¹
(* indicates equal contribution)


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Transfer learning is important for foundation models to adapt to downstream tasks. However, many foundation models are proprietary, so users must share their data with model owners to fine-tune the models, which is costly and raises privacy concerns. Moreover, fine-tuning large foundation models is computation-intensive and impractical for most downstream users. In this paper, we propose Offsite-Tuning, an efficient transfer learning framework that can adapt billion-parameter foundation models to downstream data without data and full model exchanges. In Offsite-Tuning, the model owner sends a lightweight adapter and a lossy compressed emulator to the data owner, who then fine-tunes the adapter on the downstream data with the emulator's assistance. The fine-tuned adapter is then returned to the model owner, who plugs it into the full model to create an adapted foundation model. Offsite-Tuning is computationally more efficient and preserves two parties' privacy compared with existing fine-tuning methods that require access to the full model weights. We demonstrate the effectiveness of Offsite-Tuning on various large language and vision foundation models. Offsite-Tuning can achieve comparable accuracy as full model fine-tuning while being efficient and privacy-preserving, achieving 6.5x speedup and 5.6x memory reduction.




title={Offsite-Tuning: Transfer Learning without Full Model},  

author={Xiao, Guangxuan and Lin, Ji and Han, Song},




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