Adapter Layers
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![[2106.04647] Compacter: Efficient Low-Rank Hypercomplex Adapter Layers](https://ar5iv.labs.arxiv.org/html/2106.04647/assets/x2.png)





















![Left: Illustration of adding Adapter [13] modules to a Conformer [14 ...](https://www.researchgate.net/publication/372404048/figure/fig3/AS:11431281175040857@1689563084042/Left-Illustration-of-adding-Adapter-13-modules-to-a-Conformer-14-layer-where_Q320.jpg)











![[2308.08234] Challenges and Opportunities of Using Transformer-Based ...](https://ar5iv.labs.arxiv.org/html/2308.08234/assets/figures/adapter-cl.png)




![[领域总结] [PEFT] 浅谈Adapter-tuning - 知乎](https://pic4.zhimg.com/v2-54dffcbf8e2948209eb96b78aedbb1af_r.jpg)









![[ACL 2022] PERFECT 无需人工模板的prompt learning新框架 - 知乎](https://pic1.zhimg.com/v2-90a9f2ccc74891caccb206768084b478_b.jpg)

![[2007.04297] Open Domain Suggestion Mining Leveraging Fine-Grained Analysis](https://ar5iv.labs.arxiv.org/html/2007.04297/assets/Adapter.png)

![[Paper] LoRA: Low-Rank Adaptation of Large Language Models | JJJang Blog](https://sejeongak.github.io/assets/img/LoRA/fig4.png)















![[领域总结] [PEFT] 浅谈Adapter-tuning - 知乎](https://pic2.zhimg.com/v2-4baf159ecc8c7d33312596c0b2e89581_b.jpg)


![[领域总结] [PEFT] 浅谈Adapter-tuning - 知乎](https://pic2.zhimg.com/v2-24cb29036d29b3d6b30d72f07293b8f9_r.jpg)
![[领域总结] [PEFT] 浅谈Adapter-tuning - 知乎](https://pic2.zhimg.com/v2-aca0501921febf19b0c6880cbd135bd5_b.jpg)



















