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Adapters is an add-on library to HuggingFace's Transformers, integrating 10+ adapter methods into 20+ state-of-the-art Transformer models with minimal coding overhead for training and inference. Adapters provide a unified interface for efficient fine-tuning and modular transfer learning, supporting a myriad of features like full-precision or quantized training (e.g. Q-LoRA, Q-Bottleneck Adapters, or Q-PrefixTuning), adapter merging via task arithmetics or the composition of multiple adapters via composition blocks, allowing advanced research in parameter-efficient transfer learning for NLP tasks.
Features
- Enables parameter-efficient fine-tuning of transformers
- Supports modular adapters for different NLP tasks
- Reduces memory and computational requirements
- Compatible with Hugging Face Transformers
- Allows quick adaptation to new languages and domains
- Provides a growing repository of pre-trained adapters
Project Samples
License
Apache License V2.0Follow Adapters
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