This library provides a unified interface for parameter-efficient and modular transfer learning, integrating over 10 adapter methods with more than 20 state-of-the-art Transformer models. It targets NLP researchers and practitioners seeking to efficiently fine-tune models and explore advanced techniques like adapter composition and quantization.

How It Works

Adapters acts as an add-on to HuggingFace's Transformers library, injecting lightweight adapter modules into existing Transformer architectures. This approach allows for fine-tuning models with significantly fewer trainable parameters compared to full fine-tuning. It supports advanced research through features like adapter merging via task arithmetic and composing multiple adapters using composition blocks.

Quick Start & Requirements

• Installation: pip install -U adapters
• Prerequisites: Python 3.9+, PyTorch 2.0+.
• Resources: Requires HuggingFace Transformers and PyTorch.
• Documentation: https://docs.adapterhub.ml/

Highlighted Details

• Supports over 10 adapter methods including LoRA, QLoRA, Prefix Tuning, and AdapterFusion.
• Integrates with 20+ Transformer models from HuggingFace.
• Enables adapter merging and composition for complex transfer learning scenarios.
• Offers support for quantized training methods like Q-LoRA.

Maintenance & Community

The library is actively maintained by the AdapterHub community. Further information and community channels can be found on their website.

Licensing & Compatibility

The library is compatible with HuggingFace Transformers. Licensing details are not explicitly stated in the README, but typically such libraries follow permissive licenses compatible with commercial use.

Limitations & Caveats

The library has replaced the adapter-transformers package, requiring users to transition their existing adapters. Specific licensing terms should be verified for commercial applications.