Contributing to AdapterHub

There are many ways in which you can contribute to AdapterHub and the adapters library. This includes code contributions such as:

  • implementing new adapter methods

  • adding support for new Transformer

  • fixing open issues

as well as non-code contributions such as:

  • training and uploading adapters to the Hub

  • writing documentation and blog posts

  • helping others with their issues and questions

Whichever way you’d like to contribute, you’re very welcome to do so!

Contributing to the adapters codebase

Setting up your dev environment

To get started with writing code for adapters, you’d want to set up the project on a local development environment.

adapters closely follows the original Hugging Face Transformers repository in many aspects. This guide assumes that you want to set up your dev environment on a local machine and that you have basic knowledge of git. Additionally, you require Python 3.8 or above pre-installed to get started.

In the following, we go through the setup procedure step by step:

  1. Fork the adapters repository to get a local copy of the code under your user account.

  2. Clone your fork to your local machine:

    git clone --recursive<YOUR_USERNAME>/adapters.git
    cd adapters

    Note: The --recursive flag is important to initialize git submodules.

  3. Create a virtual environment, e.g. via virtualenv or conda.

  4. Install PyTorch, following the installation command for your environment on their website.

  5. Install Hugging Face Transformers from the local git submodule:

    pip install ./hf_transformers
  6. Install adapters and required dev dependencies:

    pip install -e ".[dev]"

Adding Adapter Methods

How to integrate new efficient fine-tuning/ adapter methods to adapters is described at

Adding Adapters to a Model

How to add adapter support to a model type already supported by Hugging Face Transformers is described at

Testing your changes to the codebase

adapters provides multiple Makefile targets for easily running tests and repo checks. Make sure these checks run without errors to pass the CI pipeline tasks when you open a pull request.

To run all tests in the repository:

make test

To auto format code and imports in the whole codebase:

make style

This will run black and isort.

To run all quality checks ensuring code style and repo consistency:

make quality

This will run checks with black, isort and flake8 as well as additional custom checks.

Publishing Pre-Trained Adapters

How to make your own trained adapters accessible for the adapters library HuggingFace Model Hub is described at