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是一个开源库，将参数高效和模块化的迁移学习统一起来，通过整合10种不同的适配器方法，提供简便的使用和灵活的配置；该库通过组合模块允许研究人员和从业者利用适配器的模块化设计复杂的适配器设置；通过在各种自然语言处理任务上评估其性能，我们展示了该库的有效性；Adapters为解决传统微调范式的挑战和促进更高效和模块化的迁移学习提供了一个强大的工具；该库可通过此链接获取。

适配器：一种参数高效与模块化迁移学习的统一库