offsite-tuning
Offsite-Tuning presents an innovative transfer learning framework designed to enhance privacy and computational efficiency. It enables the adaptation of large-scale foundation models to specific tasks without requiring full model access, effectively addressing traditional cost and privacy concerns. A lightweight adapter and a compressed emulator are provided for local fine-tuning, maintaining accuracy while significantly improving speed and reducing memory usage. This approach is validated on various large language and vision models, providing a practical solution for environments prioritizing privacy and resource constraints.
