Adapters

A Unified Library for Parameter-Efficient and Modular Transfer Learning

Website   •   Documentation   •   Paper

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 provides 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.

Note: The Adapters library has replaced the adapter-transformers package. All previously trained adapters are compatible with the new library. For transitioning, please read: https://docs.adapterhub.ml/transitioning.html.

Installation

adapters currently supports Python 3.8+ and PyTorch 1.10+. After installing PyTorch, you can install adapters from PyPI ...

pip install -U adapters

... or from source by cloning the repository:

git clone https://github.com/adapter-hub/adapters.git
cd adapters
pip install .

Quick Tour

Load pre-trained 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)

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Adapt existing model setups:

import adapters
from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained("t5-base")

adapters.init(model)

model.add_adapter("my_lora_adapter", config="lora")
model.train_adapter("my_lora_adapter")

# Your regular training loop...

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Flexibly configure adapters:

from adapters import ConfigUnion, PrefixTuningConfig, ParBnConfig, AutoAdapterModel

model = AutoAdapterModel.from_pretrained("microsoft/deberta-v3-base")

adapter_config = ConfigUnion(
    PrefixTuningConfig(prefix_length=20),
    ParBnConfig(reduction_factor=4),
)
model.add_adapter("my_adapter", config=adapter_config, set_active=True)

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Easily compose adapters in a single model:

from adapters import AdapterSetup, AutoAdapterModel
import adapters.composition as ac

model = AutoAdapterModel.from_pretrained("roberta-base")

qc = model.load_adapter("AdapterHub/roberta-base-pf-trec")
sent = model.load_adapter("AdapterHub/roberta-base-pf-imdb")

with AdapterSetup(ac.Parallel(qc, sent)):
    print(model(**tokenizer("What is AdapterHub?", return_tensors="pt")))

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Useful Resources

HuggingFace's great documentation on getting started with Transformers can be found here. adapters is fully compatible with Transformers.

To get started with adapters, refer to these locations:

• Colab notebook tutorials, a series notebooks providing an introduction to all the main concepts of (adapter-)transformers and AdapterHub
• https://docs.adapterhub.ml, our documentation on training and using adapters with adapters
• https://adapterhub.ml to explore available pre-trained adapter modules and share your own adapters
• Examples folder of this repository containing HuggingFace's example training scripts, many adapted for training adapters

Implemented Methods

Currently, adapters integrates all architectures and methods listed below:

Method	Paper(s)	Quick Links
Bottleneck adapters	Houlsby et al. (2019) Bapna and Firat (2019)	Quickstart, Notebook
AdapterFusion	Pfeiffer et al. (2021)	Docs: Training, Notebook
MAD-X, Invertible adapters	Pfeiffer et al. (2020)	Notebook
AdapterDrop	Rücklé et al. (2021)	Notebook
MAD-X 2.0, Embedding training	Pfeiffer et al. (2021)	Docs: Embeddings, Notebook
Prefix Tuning	Li and Liang (2021)	Docs
Parallel adapters, Mix-and-Match adapters	He et al. (2021)	Docs
Compacter	Mahabadi et al. (2021)	Docs
LoRA	Hu et al. (2021)	Docs
(IA)^3	Liu et al. (2022)	Docs
UniPELT	Mao et al. (2022)	Docs
Prompt Tuning	Lester et al. (2021)	Docs
QLoRA	Dettmers et al. (2023)	Notebook
ReFT	Wu et al. (2024)	Docs
Adapter Task Arithmetics	Chronopoulou et al. (2023) Zhang et al. (2023)	Docs, Notebook

Supported Models

We currently support the PyTorch versions of all models listed on the Model Overview page in our documentation.

Developing & Contributing

To get started with developing on Adapters yourself and learn more about ways to contribute, please see https://docs.adapterhub.ml/contributing.html.

Citation

If you use Adapters in your work, please consider citing our library paper: Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning

@inproceedings{poth-etal-2023-adapters,
    title = "Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning",
    author = {Poth, Clifton  and
      Sterz, Hannah  and
      Paul, Indraneil  and
      Purkayastha, Sukannya  and
      Engl{\"a}nder, Leon  and
      Imhof, Timo  and
      Vuli{\'c}, Ivan  and
      Ruder, Sebastian  and
      Gurevych, Iryna  and
      Pfeiffer, Jonas},
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-demo.13",
    pages = "149--160",
}

Alternatively, for the predecessor adapter-transformers, the Hub infrastructure and adapters uploaded by the AdapterHub team, please consider citing our initial paper: AdapterHub: A Framework for Adapting Transformers

@inproceedings{pfeiffer2020AdapterHub,
    title={AdapterHub: A Framework for Adapting Transformers},
    author={Pfeiffer, Jonas and
            R{\"u}ckl{\'e}, Andreas and
            Poth, Clifton and
            Kamath, Aishwarya and
            Vuli{\'c}, Ivan and
            Ruder, Sebastian and
            Cho, Kyunghyun and
            Gurevych, Iryna},
    booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
    pages={46--54},
    year={2020}
}