From 8eae30d9abe73d4ef11ab2e46abb1bbd5034584a Mon Sep 17 00:00:00 2001
From: Song <3068011+songhan@users.noreply.github.com>
Date: Wed, 8 Feb 2023 22:24:00 -0500
Subject: [PATCH] Update README.md

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 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

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@@ -3,7 +3,7 @@ The official implementation of Offsite-Tuning.
 
 ## Abstract
 
-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. We propose Offsite-Tuning, an efficient and privacy-preserving 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 is computationally efficient and better preserves both parties’ privacy 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 6.5× speedup and 5.6× memory reduction.
+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 model is computation-intensive and impractical for most downstream users. We propose Offsite-Tuning, an efficient and privacy-preserving 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 is computationally efficient and better preserves both parties’ privacy 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 6.5× speedup and 5.6× memory reduction.
 
 ## Setup