adapters
A Unified Library for Parameter-Efficient and Modular Transfer Learning
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It solves a real problem—efficient fine-tuning of large models—with a well-documented, unified interface. Install it if you're doing transfer learning on transformer models and want to reduce training costs and memory usage.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9+ and PyTorch 2.0+
- Low install friction with only 2 runtime dependencies (transformers and packaging).
- The package is actively maintained with a recent release and no known vulnerabilities.
License · maintenance · safety
Apache (permissive) — Licensed under Apache (permissive), allowing commercial and private use with minimal restrictions.
last release 2026-04-26 (110 days) · last repo commit 2026-04-26 · 2,824 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 82,826 downloads/mo, #14,130 on PyPI
Alternatives
Verify before relying
pip install -U adapters
from adapters import AutoAdapterModel
from transformers import AutoTokenizer
model = AutoAdapterModel.from_pretrained("roberta-base")
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
model.load_adapter("AdapterHub/roberta-base-pf-imdb", source="hf", set_active=True)
print(model(**tokenizer("This works great!", return_tensors="pt")).logits)- Whether all 10+ adapter methods mentioned are fully functional in version 1.3.0
- Compatibility scope with the full range of 20+ Transformer models claimed
- Performance characteristics and memory savings compared to full model fine-tuning
What it is and what it does
Adapters is an extension library for HuggingFace Transformers that integrates parameter-efficient fine-tuning methods into transformer models. Instead of updating all model weights during training, adapters add small trainable modules (like LoRA, prefix tuning, or bottleneck adapters) that achieve competitive performance with far fewer parameters. The library provides a unified interface for training these adapters, loading pre-trained ones from AdapterHub, and composing multiple adapters together for multi-task or ensemble scenarios.
The package depends on transformers and packaging, and requires Python 3.9+ and PyTorch 2.0+. It's designed for researchers and practitioners working on NLP tasks who want efficient fine-tuning without the memory and compute costs of full model updates. You can use it to adapt existing model setups, load community-shared adapters, or combine multiple adapters in a single model using composition blocks.
Use it for
- Fine-tune large language models on domain-specific tasks with minimal GPU memory by training only adapter weights instead of all parameters.
- Load and apply pre-trained adapters from AdapterHub to perform sentiment analysis, question classification, or other NLP tasks on models like RoBERTa or T5.
- Compose multiple task-specific adapters in parallel or sequence to handle multi-task learning or ensemble predictions in a single model.
- Experiment with different parameter-efficient methods (LoRA, prefix tuning, bottleneck adapters) on the same base model using a unified configuration interface.
- Train quantized adapters (Q-LoRA, Q-Bottleneck) for even smaller memory footprints on resource-constrained hardware.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It solves a real problem—efficient fine-tuning of large models—with a well-documented, unified interface. Install it if you're doing transfer learning on transformer models and want to reduce training costs and memory usage.
Install
adapters on PyPI
Before you install
Low install friction with only 2 runtime dependencies (transformers and packaging). The package is actively maintained with a recent release and no known vulnerabilities.
Requires Python 3.9+ and PyTorch 2.0+
License in practice
Licensed under Apache (permissive), allowing commercial and private use with minimal restrictions.
Quickstart
pip install -U adapters
from adapters import AutoAdapterModel
from transformers import AutoTokenizer
model = AutoAdapterModel.from_pretrained("roberta-base")
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
model.load_adapter("AdapterHub/roberta-base-pf-imdb", source="hf", set_active=True)
print(model(**tokenizer("This works great!", return_tensors="pt")).logits)Verify before relying
- Whether all 10+ adapter methods mentioned are fully functional in version 1.3.0
- Compatibility scope with the full range of 20+ Transformer models claimed
- Performance characteristics and memory savings compared to full model fine-tuning
Package facts
| License | Apache permissive |
| Python support | Supports the current Python release >=3.9.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagestransformerspackaging |
| Maintenance | Actively maintained 110 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 82,826 / month, #14,130 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: adapters-1.3.0-py3-none-any.whl
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