37 lines
1.9 KiB
Markdown
37 lines
1.9 KiB
Markdown
# What is Delta-tuning and Why OpenDelta?
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(WhatisDelta)=
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:::{admonition} What is Delta?
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:class: tip
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As Pre-trained language models (PLMs) have become the fundamental infrastructure on many NLP tasks and benchmarks, it is becoming increasingly clear from recent research that **larger models tend to lead to better performance**. However, large-scale PLMs also bring prohibitive adaptation costs when fine-tuning all the parameters of a model and retaining separate instances for different tasks.
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**Parameter-efficient model stimulation methods** thus have attracted researchers' eyes, which only tune a small fraction of model parameter while achieving comparable or even better performance than full-model fine-tuning, dubbed as "Delta-tuning".
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**Delta** thus means a small fraction $\Delta\Theta$ of parameters besides the pretrained models $\Theta_0$.
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\begin{gather*}
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\Theta \sim \Theta_0\text{(frozen)} + \Delta\Theta\text{(tunable)}
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\end{gather*}
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This open-source project implement several delta-tuning methods, which allows researchers and engineers to quickly migrate their codes from full-model tuning to delta-tuning without replace the backend (the implementation of the backbone PLM).
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:::
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## Why OpenDelta?
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- <span style="color:rgb(81, 217, 245);font-weight:bold">Clean:</span> No need to edit the backbone PTM’s codes.
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- <span style="color:orange;font-weight:bold">Simple:</span> Migrating from full-model tuning to delta-tuning needs as little as 3 lines of codes.
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- <span style="color:green;font-weight:bold">Sustainable:</span> Most evolution in external library doesn’t require a new OpenDelta.
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- <span style="color:red;font-weight:bold">Extendable:</span> Various PTMs can share the same delta-tuning codes.
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- <span style="color:purple;font-weight:bold">Flexible:</span> Able to apply delta-tuning to (almost) any position of the PTMs.
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## Delta-tuning papers
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<img src="../imgs/todo-icon.jpeg" height="30px">
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