Everyone is welcome to contribute, and we value everybody's contribution. Code contributions are not the only way to help the community. Answering questions, helping others, and improving the documentation are also immensely valuable.

It also helps us if you spread the word! Reference the library in blog posts about the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply ⭐️ the repository to say thank you.

This guide was heavily inspired by the awesome scikit-learn guide to contributing and our friends at transformers.

There are several ways you can contribute to 🤗 Transformers.js:

• Fix outstanding issues with the existing code.
• Submit issues related to bugs or desired new features.
• Implement new models.
• Contribute to the examples or to the documentation.

If you notice an issue with the existing code and have a fix in mind, feel free to start contributing and open a Pull Request!

Do your best to follow these guidelines when submitting a bug-related issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback.

The 🤗 Transformers.js library is robust and reliable thanks to users who report the problems they encounter.

Before you report an issue, we would really appreciate it if you could make sure the bug was not already reported (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code.

To create a new issue, please use one of the templates we prepared for you. Most likely the Bug Report.

If there is a new feature you'd like to see in 🤗 Transformers.js, please open an issue and describe:

1. What is the motivation behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community? Whatever it is, we'd love to hear about it!
2. Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you.
3. Provide a code snippet that demonstrates the feature's usage.
4. If the feature is related to a paper, please include a link.

If your issue is well written we're already 80% of the way there by the time you create it.

We have added a template to help you get started with your issue.

New models are constantly released and if you want to request support for a new model, please use the template for new model requests.

If you are willing to contribute the model yourself, let us know so we can help you add it to 🤗 Transformers.js! The process of adding support for a new model architecture has three main phases: exporting the model to ONNX, then wiring it into the library, and finally adding tests.

Transformers.js runs models using ONNX Runtime. Before adding a model to the library, you need an ONNX export of it.

• For LLMs, we recommend exporting with microsoft/onnxruntime-genai
• For other models, we recommend exporting with huggingface/optimum-onnx

Once exported, upload the ONNX files to the Hugging Face Hub alongside the model's original config, tokenizer, and other preprocessing files so they can be loaded with from_pretrained.

Every model in Transformers.js is built from the same pieces:

• A model class: extends PreTrainedModel, which handles all ONNX inference, generation, and KV-cache management
• Task head classes: thin wrappers that wrap the output in the right output object (e.g. MaskedLMOutput)
• A tokenizer and/or processor: only needed if the model requires a custom one; most models reuse an existing class

All model files live under packages/transformers/src/models/<model_type>/. Look at an existing model of the same type to understand what's needed; most are just a few lines.

Every model file exports a base class and one or more task heads. For the vast majority of models, these are empty subclasses. All the logic lives in PreTrainedModel.

Decoder-only LLM:

import { PreTrainedModel } from '../modeling_utils.js';

export class MyModelPreTrainedModel extends PreTrainedModel {}
export class MyModelModel extends MyModelPreTrainedModel {}
export class MyModelForCausalLM extends MyModelPreTrainedModel {}

Encoder-only model:

import { PreTrainedModel } from '../modeling_utils.js';
import { MaskedLMOutput, SequenceClassifierOutput } from '../modeling_outputs.js';

export class MyModelPreTrainedModel extends PreTrainedModel {}
export class MyModelModel extends MyModelPreTrainedModel {}

export class MyModelForMaskedLM extends MyModelPreTrainedModel {
    async _call(model_inputs) {
        return new MaskedLMOutput(await super._call(model_inputs));
    }
}

export class MyModelForSequenceClassification extends MyModelPreTrainedModel {
    async _call(model_inputs) {
        return new SequenceClassifierOutput(await super._call(model_inputs));
    }
}

Only add the task heads the model actually supports. The available output classes (MaskedLMOutput, TokenClassifierOutput, Seq2SeqLMOutput, etc.) are all in modeling_outputs.js.

Most models reuse an existing tokenizer (e.g. all Llama-family models use LlamaTokenizer). Only create a new one if the model genuinely needs custom tokenization or preprocessing logic.

The class name must match the tokenizer_class or processor_class field in the model's tokenizer_config.json / preprocessor_config.json on the Hub.

Once the model file is written, three more files need updating:

1. src/models/models.js: add export * from './<name>/modeling_<name>.js'
2. src/models/registry.js: map the model_type string (from config.json) to the class names, and set the correct loading category (EncoderOnly, DecoderOnly, Seq2Seq, etc.)
3. src/configs.js: for generative models, add a case in getNormalizedConfig() to map the model's config field names to the normalized names the KV-cache runtime expects

Look at a similar existing model in each file to see exactly what to add.

Create packages/transformers/tests/models/<model_type>/test_modeling_<model_type>.js. The test runner auto-discovers files by this naming convention. No registration needed.

Use a small, fast model. The convention is to use a tiny-random-* model from hf-internal-testing/ on the Hub. If one doesn't exist for your architecture, generate one with the transformers Python library:

from transformers import AutoConfig, AutoModelForCausalLM
config = AutoConfig.for_model("my_model", num_hidden_layers=2, hidden_size=64, ...)
model = AutoModelForCausalLM.from_config(config)
model.push_to_hub("hf-internal-testing/tiny-random-MyModelForCausalLM")

Test file structure:

import { MyModelForCausalLM, MyModelTokenizer } from "../../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js";

export default () => {
    describe("MyModelForCausalLM", () => {
        const model_id = "hf-internal-testing/tiny-random-MyModelForCausalLM";
        let model, tokenizer;

        beforeAll(async () => {
            model = await MyModelForCausalLM.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
            tokenizer = await MyModelTokenizer.from_pretrained(model_id);
        }, MAX_MODEL_LOAD_TIME);

        it("batch_size=1", async () => {
            const inputs = tokenizer("hello");
            const outputs = await model.generate({ ...inputs, max_length: 10 });
            expect(outputs.tolist()).toEqual([[/* expected token ids */]]);
        }, MAX_TEST_EXECUTION_TIME);

        it("batch_size>1", async () => {
            const inputs = tokenizer(["hello", "hello world"], { padding: true });
            const outputs = await model.generate({ ...inputs, max_length: 10 });
            expect(outputs.tolist()).toEqual([[...], [...]]);
        }, MAX_TEST_EXECUTION_TIME);

        afterAll(async () => { await model?.dispose(); }, MAX_MODEL_DISPOSE_TIME);
    });
};

Run your tests with:

# All tests
pnpm test

# Only your model's tests
pnpm --filter @huggingface/transformers test -t "MyModelForCausalLM"

Before writing any code, we strongly advise you to search through the existing PRs or issues to make sure nobody is already working on the same thing. If you are unsure, it is always a good idea to open an issue to get some feedback.

You will need basic git proficiency to contribute to 🤗 Transformers.js. While git is not the easiest tool to use, it has the greatest manual. Type git --help in a shell and enjoy! If you prefer books, Pro Git is a very good reference.

You'll need the following tools installed to contribute to 🤗 Transformers.js:

• Node.js v18 or above
• pnpm - Fast, disk space efficient package manager

To install pnpm:

npm install -g pnpm

Follow the steps below to start contributing:

1. Fork the repository by clicking on the Fork button on the repository's page. This creates a copy of the code under your GitHub user account.

2. Clone your fork to your local disk, and add the base repository as a remote:

   git clone git@github.com:<your Github handle>/transformers.js.git
   cd transformers.js

3. Create a new branch to hold your development changes:

   git checkout -b a-descriptive-name-for-my-changes

🚨 Do not work on the main branch!

4. Set up a development environment by running the following command:

   pnpm install

5. Develop the features in your branch.
6. Now you can go to your fork of the repository on GitHub and click on Pull Request to open a pull request. Make sure you tick off all the boxes on our checklist below. When you're ready, you can send your changes to the project maintainers for review.
7. It's ok if maintainers request changes, it happens to our core contributors too! So everyone can see the changes in the pull request, work in your local branch and push the changes to your fork. They will automatically appear in the pull request.

☐ The pull request title should summarize your contribution.
☐ If your pull request addresses an issue, please mention the issue number in the pull request description to make sure they are linked (and people viewing the issue know you are working on it).
☐ To indicate a work in progress please prefix the title with [WIP]. These are useful to avoid duplicated work, and to differentiate it from PRs ready to be merged.
☐ Make sure existing tests pass (pnpm test).
☐ Make sure the build completes successfully (pnpm build).
☐ Make sure your code is formatted properly with Prettier (pnpm format:check).
☐ If adding a new feature, also add tests for it.
☐ If your changes affect user-facing functionality, update the relevant documentation.

We are using Jest to execute unit-tests. All tests can be found in packages/transformers/tests and have to end with .test.js

Execute all tests

pnpm test

Execute tests for a specific package

pnpm --filter @huggingface/transformers test

Execute a specific test file

cd packages/transformers
pnpm test -- ./tests/models.test.js

We use Prettier to maintain consistent code formatting across the project. Please ensure your code is formatted before submitting a pull request.

Format all files:

pnpm format

Check formatting without making changes:

pnpm format:check

IDE Integration (recommended)

We recommend setting up Prettier in your IDE to format on save:

Visual Studio Code:

1. Install the Prettier extension
2. Open Settings (Ctrl+, or Cmd+,)
3. Search for "format on save"
4. Enable "Editor: Format On Save"
5. Set Prettier as your default formatter: search for "default formatter" and select "Prettier - Code formatter"

IntelliJ IDEA / WebStorm:

1. Go to Settings → Languages & Frameworks → JavaScript → Prettier
2. Set the Prettier package path (usually node_modules/prettier)
3. Check "On save" under "Run for files"
4. Add file patterns: {**/*,*}.{js,ts,jsx,tsx,json,css,scss,md}
5. Click "Apply" and "OK"

This project uses pnpm workspaces to manage multiple packages in a monorepo. Currently, there is one workspace:

• packages/transformers - The main Transformers.js library

This structure allows for better organization and makes it easier to add framework-specific integrations in the future.

The recommended way to develop and test changes is to use the watch mode build and install from the local package:

1. Start the build in watch mode:

   pnpm dev

   This will automatically rebuild the library whenever you make changes to the source code.

2. Create a separate test project and install transformers.js from your local development directory:

   mkdir my-test-project
   cd my-test-project
   npm init -y
   npm install file:/path/to/transformers.js/packages/transformers

   Replace /path/to/transformers.js with the actual path to your cloned repository.

3. Make your changes to the transformers.js source code in the main repository. The watch mode will automatically rebuild the library.

4. Test your changes in your test project. The changes will be automatically reflected since the package is linked via the file: protocol.

This workflow allows for rapid iteration and testing during development.