Adapters is an add-on library extending HuggingFace Transformers with 10+ adapter methods integrated into 20+ state-of-the-art Transformer models. It provides a unified interface for efficient fine-tuning, supporting techniques like Q-LoRA and quantized training to reduce computational overhead. The library enables modular transfer learning where task-specific adapters can be trained independently and combined flexibly.