title: Adapters: A Unified Library for Parameter-Efficient and Modular Transfer
    Learning
summary: 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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paper_authors.name: Hannah Sterz
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paper_authors.name: Indraneil Paul
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paper_authors.user: iNeil77
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paper_authors.name: Sukannya Purkayastha
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paper_authors.fullname: Sukannya Purkayastha
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paper_authors.name: Leon Engländer
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paper_authors.fullname: Leon Engländer
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paper_authors.name: Timo Imhof
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paper_authors.fullname: Timo Imhof
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paper_authors.user: TimoImhof
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paper_authors.name: Ivan Vulić
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paper_authors.fullname: Ivan Vulić
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paper_authors.user: ivulic
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paper_authors.name: Sebastian Ruder
paper_authors.status: admin_assigned
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paper_authors._id: 624575789dc26c59bae6a19e
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paper_authors.fullname: Sebastian Ruder
paper_authors.type: user
paper_authors.user: ruder
paper_authors._id: 655c2228b20e0c0ee95b23e7
paper_authors.name: Iryna Gurevych
paper_authors._id: 655c2228b20e0c0ee95b23e8
paper_authors.name: Jonas Pfeiffer
paper_title: Adapters: A Unified Library for Parameter-Efficient and Modular Transfer
    Learning
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Adapters parameter-efficient fine-tuning modular transfer learning large language models unified interface adapter methods composition blocks NLP tasks