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:

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}
}