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.

翻译：摘要：我们推出了Adapters，一个开源库，旨在统一大型语言模型中的参数高效与模块化迁移学习。通过将10种不同的适配器方法集成到统一接口中，Adapters提供了易用性与灵活配置。本库使研究人员和开发者能够利用组合模块实现适配器模块化，从而设计复杂的适配器配置。我们通过在全参数微调范式下对多种自然语言处理任务进行评估，验证了该库的有效性。Adapters为应对传统微调范式的挑战、推动更高效与模块化的迁移学习提供了有力工具。本库可通过https://adapterhub.ml/adapters获取。