adapter-hub/adapters
A Unified Library for Parameter-Efficient and Modular Transfer Learning
Integrates 10+ parameter-efficient fine-tuning methods (LoRA, prefix tuning, bottleneck adapters, etc.) into 20+ HuggingFace Transformer models via a unified API. Supports advanced composition patterns like adapter merging via task arithmetic and parallel/sequential adapter stacking, plus quantized training variants (Q-LoRA, Q-Bottleneck). Built as a drop-in extension to the Transformers library with minimal code changes needed for both training and inference.
2,802 stars and 86,888 monthly downloads. Used by 2 other packages. Actively maintained with 1 commit in the last 30 days. Available on PyPI.
Stars
2,802
Forks
375
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 01, 2026
Monthly downloads
86,888
Commits (30d)
1
Dependencies
2
Reverse dependents
2
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