From ced97746ebb8fb0cd4da8d2e08a616d5689a5c7a Mon Sep 17 00:00:00 2001 From: Guangxuan-Xiao Date: Thu, 9 Feb 2023 23:24:13 +0000 Subject: [PATCH] update readme --- README.md | 8 +++----- figures/cv_results.png | Bin 170815 -> 0 bytes 2 files changed, 3 insertions(+), 5 deletions(-) delete mode 100644 figures/cv_results.png diff --git a/README.md b/README.md index 44e1421..4a502a9 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Offsite-Tuning: Transfer Learning without Full Model [[paper]()] -![overview](figures/overview.png) + ## Abstract @@ -38,7 +38,7 @@ In this repository, you will find all the necessary components to reproduce the ## Results - Comparing existing fine-tuning approaches (top and middle) and Offsite-Tuning (bottom). (a) Traditionally, users send labeled data to model owners for fine-tuning, raising privacy concerns and incurring high computational costs. (b) Model owner sending the full model to the data owner is not practical, which threatens the ownership of the proprietary model, and it's not affordable for users to fine-tune the huge foundation model due to resource constraints. (c) Offsite-tuning offers a privacy-preserving and efficient alternative to traditional fine-tuning methods that require access to full model weights. -![paradigm](figures/paradigm.png) + - On 1-billion scale language models, Offsite-tuning (OT Plug-in) improves zero-shot (ZS) performance across all tasks, with only slight decreases compared to full fine-tuning (FT). Also, a consistent performance gap is observed between the emulator fine-tuning and plug-in, indicating offsite-tuning effectively preserves the privacy of the original proprietary model (users can not use the emulator to achieve the same performance). ![lm_results](figures/lm_results.png) @@ -46,11 +46,9 @@ In this repository, you will find all the necessary components to reproduce the - Offsite-tuning also works on language models over 6-billion parameters. ![llm_results](figures/llm_results.png) -- Offsite-tuning also works on vision foundation models. -![cv_results](figures/cv_results.png) - Offsite-Tuning significantly increase the fine-tuning throughput and reduce the memory footprint compared to the existing fine-tuning methods. -![efficiency](figures/efficiency.png) + ## Citation diff --git a/figures/cv_results.png b/figures/cv_results.png deleted file mode 100644 index b918b16addc173942fbba95e4300ded0829249bd..0000000000000000000000000000000000000000 GIT binary patch literal 0 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