Adapters: A Unified Library for Parameter-Efficient and Modular Transfer
  Learning
We introduce Adapters, an open-source library that unifies
parameter-efficient and modular transfer learning in large language models. By
integrating 10 diverse adapter methods into a unified interface, Adapters
offers ease of use and flexible configuration. Our library allows researchers
and practitioners to leverage adapter modularity through composition blocks,
enabling the design of complex adapter setups. We demonstrate the library's
efficacy by evaluating its performance against full fine-tuning on various NLP
tasks. Adapters provides a powerful tool for addressing the challenges of
conventional fine-tuning paradigms and promoting more efficient and modular
transfer learning. The library is available via https://adapterhub.ml/adapters.
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Clifton Poth
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2023-11-21T15:00:26.821Z
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calpt
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calpt
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Hannah Sterz
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Indraneil Paul
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2023-11-21T13:11:27.010Z
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Indraneil Paul
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iNeil77
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Sukannya Purkayastha
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2023-11-21T13:11:35.184Z
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Sukannya Purkayastha
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sukannya
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Leon Engländer
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2023-11-21T13:11:42.030Z
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Leon Engländer
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Timo Imhof
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2023-11-21T13:11:49.097Z
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Timo Imhof
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TimoImhof
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Ivan Vulić
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2023-11-21T13:11:58.391Z
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Ivan Vulić
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Sebastian Ruder
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2023-11-21T13:12:05.553Z
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Sebastian Ruder
user
ruder
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Iryna Gurevych
655c2228b20e0c0ee95b23e8
Jonas Pfeiffer
Adapters: A Unified Library for Parameter-Efficient and Modular Transfer
  Learning
We introduce Adapters, an open-source library that unifies
parameter-efficient and modular transfer learning in large language models. By
integrating 10 diverse adapter methods into a unified interface, Adapters
offers ease of use and flexible configuration. Our library allows researchers
and practitioners to leverage adapter modularity through composition blocks,
enabling the design of complex adapter setups. We demonstrate the library's
efficacy by evaluating its performance against full fine-tuning on various NLP
tasks. Adapters provides a powerful tool for addressing the challenges of
conventional fine-tuning paradigms and promoting more efficient and modular
transfer learning. The library is available via https://adapterhub.ml/adapters.
Adapters is an open-source library designed for parameter-efficient and modular transfer learning in large language models, offering a unified interface for various adapter methods.
Adapters parameter-efficient fine-tuning modular transfer learning large language models unified interface adapter methods composition blocks NLP tasks