  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 raise
privacy concerns. Moreover, fine-tuning large foundation models is
computation-intensive and impractical for most downstream users. In this paper,
we propose Offsite-Tuning, a privacy-preserving and efficient transfer learning
framework that can adapt billion-parameter foundation models to downstream data
without access to the full model. In offsite-tuning, the model owner sends a
light-weight 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 preserves both parties' privacy and is computationally more
efficient than the 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 privacy-preserving
and efficient, achieving 6.5x speedup and 5.6x memory reduction. Code is
available at https://github.com/mit-han-lab/offsite-tuning.
