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# How to contribute to transformers?
Everyone is welcome to contribute, and we value everybody's contribution. Code
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is thus not the only way to help the community. Answering questions, helping
others, reaching out and improving the documentations are immensely valuable to
the community.
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It also helps us if you spread the word: reference the library from blog posts
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on the awesome projects it made possible, shout out on Twitter every time it has
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helped you, or simply star the repo to say "thank you".
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Whichever way you choose to contribute, please be mindful to respect our
[code of conduct ](https://github.com/huggingface/transformers/blob/master/CODE_OF_CONDUCT.md ).
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## You can contribute in so many ways!
There are 4 ways you can contribute to transformers:
* Fixing outstanding issues with the existing code;
* Implementing new models;
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* Contributing to the examples or to the documentation;
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* Submitting issues related to bugs or desired new features.
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In particular there is a special [Good First
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Issue](https://github.com/huggingface/transformers/contribute) listing. It will give you a list of
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open Issues that are open to anybody to work on. Just comment in the issue that you'd like to work
on it. In that same listing you will also find some Issues with `Good Second Issue` label. These are
typically slightly more complicated than the Issues with just `Good First Issue` label. But if you
feel you know what you're doing, go for it.
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*All are equally valuable to the community.*
## Submitting a new issue or feature request
Do your best to follow these guidelines when submitting an issue or a feature
request. It will make it easier for us to come back to you quickly and with good
feedback.
### Did you find a bug?
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The 🤗 Transformers library is robust and reliable thanks to the users who notify us of
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the problems they encounter. So thank you for reporting an issue.
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First, we would really appreciate it if you could **make sure the bug was not
already reported** (use the search bar on Github under Issues).
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Did not find it? :( So we can act quickly on it, please follow these steps:
* Include your **OS type and version** , the versions of **Python** , **PyTorch** and
**Tensorflow** when applicable;
* A short, self-contained, code snippet that allows us to reproduce the bug in
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less than 30s;
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* Provide the *full* traceback if an exception is raised.
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To get the OS and software versions automatically, you can run the following command:
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```bash
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transformers-cli env
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```
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or from the root of the repository the following command:
```bash
python src/transformers/commands/transformers_cli.py env
```
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### Do you want to implement a new model?
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Awesome! Please provide the following information:
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* Short description of the model and link to the paper;
* Link to the implementation if it is open-source;
* Link to the model weights if they are available.
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If you are willing to contribute the model yourself, let us know so we can best
guide you.
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We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them
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in the [`templates` ](https://github.com/huggingface/transformers/tree/master/templates ) folder.
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### Do you want a new feature (that is not a model)?
A world-class feature request addresses the following points:
1. Motivation first:
* Is it related to a problem/frustration with the library? If so, please explain
why. Providing a code snippet that demonstrates the problem is best.
* Is it related to something you would need for a project? We'd love to hear
about it!
* Is it something you worked on and think could benefit the community?
Awesome! Tell us what problem it solved for you.
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2. Write a *full paragraph* describing the feature;
3. Provide a **code snippet** that demonstrates its future use;
4. In case this is related to a paper, please attach a link;
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5. Attach any additional information (drawings, screenshots, etc.) you think may help.
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If your issue is well written we're already 80% of the way there by the time you
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post it.
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We have added **templates** to guide you in the process of adding a new example script for training or testing the
models in the library. You can find them in the [`templates` ](https://github.com/huggingface/transformers/tree/master/templates )
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folder.
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## Start contributing! (Pull Requests)
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Before writing code, we strongly advise you to search through the existing PRs or
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issues to make sure that 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.
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You will need basic `git` proficiency to be able to contribute to
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🤗 Transformers. `git` is not the easiest tool to use but it has the greatest
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manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
Git](https://git-scm.com/book/en/v2) is a very good reference.
Follow these steps to start contributing:
1. Fork the [repository ](https://github.com/huggingface/transformers ) by
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clicking on the 'Fork' button on the repository's page. This creates a copy of the code
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under your GitHub user account.
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2. Clone your fork to your local disk, and add the base repository as a remote:
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```bash
$ git clone git@github.com:< your Github handle > /transformers.git
$ cd transformers
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$ git remote add upstream https://github.com/huggingface/transformers.git
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```
3. Create a new branch to hold your development changes:
```bash
$ git checkout -b a-descriptive-name-for-my-changes
```
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**Do not** work on the `master` branch.
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4. Set up a development environment by running the following command in a virtual environment:
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```bash
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$ pip install -e ".[dev]"
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```
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(If transformers was already installed in the virtual environment, remove
it with `pip uninstall transformers` before reinstalling it in editable
mode with the `-e` flag.)
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To run the full test suite, you might need the additional dependency on `datasets` which requires a separate source
install:
```bash
$ git clone https://github.com/huggingface/datasets
$ cd datasets
$ pip install -e .
```
If you have already cloned that repo, you might need to `git pull` to get the most recent changes in the `datasets`
library.
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5. Develop the features on your branch.
As you work on the features, you should make sure that the test suite
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passes. You should run the tests impacted by your changes like this:
```bash
$ pytest tests/< TEST_TO_RUN > .py
```
You can also run the full suite with the following command, but it takes
a beefy machine to produce a result in a decent amount of time now that
Transformers has grown a lot. Here is the command for it:
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```bash
$ make test
```
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For more information about tests, check out the
[dedicated documentation ](https://huggingface.co/docs/transformers/testing )
🤗 Transformers relies on `black` and `isort` to format its source code
consistently. After you make changes, apply automatic style corrections and code verifications
that can't be automated in one go with:
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```bash
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$ make fixup
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```
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This target is also optimized to only work with files modified by the PR you're working on.
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If you prefer to run the checks one after the other, the following command apply the
style corrections:
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```bash
$ make style
```
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🤗 Transformers also uses `flake8` and a few custom scripts to check for coding mistakes. Quality
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control runs in CI, however you can also run the same checks with:
```bash
$ make quality
```
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Finally we have a lot of scripts that check we didn't forget to update
some files when adding a new model, that you can run with
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```bash
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$ make repo-consistency
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```
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To learn more about those checks and how to fix any issue with them, check out the
[documentation ](https://huggingface.co/docs/transformers/pr_checks )
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If you're modifying documents under `docs/source` , make sure to validate that
they can still be built. This check also runs in CI. To run a local check
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make sure you have installed the documentation builder requirements. First you will need to clone the
repository containing our tools to build the documentation:
```bash
$ pip install git+https://github.com/huggingface/doc-builder
```
Then, make sure you have all the dependencies to be able to build the doc with:
```bash
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$ pip install ".[docs]"
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```
Finally run the following command from the root of the repository:
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```bash
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$ doc-builder build transformers docs/source/ --build_dir ~/tmp/test-build
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```
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This will build the documentation in the `~/tmp/test-build` folder where you can inspect the generated
Markdown files with your favorite editor. You won't be able to see the final rendering on the website
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before your PR is merged, we are actively working on adding a tool for this.
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Once you're happy with your changes, add changed files using `git add` and
make a commit with `git commit` to record your changes locally:
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```bash
$ git add modified_file.py
$ git commit
```
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Please write [good commit
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messages](https://chris.beams.io/posts/git-commit/).
It is a good idea to sync your copy of the code with the original
repository regularly. This way you can quickly account for changes:
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```bash
$ git fetch upstream
$ git rebase upstream/master
```
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Push the changes to your account using:
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```bash
$ git push -u origin a-descriptive-name-for-my-changes
```
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6. Once you are satisfied (**and the checklist below is happy too**), go to the
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webpage of your fork on GitHub. Click on 'Pull request' to send your changes
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to the project maintainers for review.
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7. It's ok if maintainers ask you for changes. It happens to 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.
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### Checklist
1. The title of your pull request should be a summary of its contribution;
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2. If your pull request addresses an issue, please mention the issue number in
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the pull request description to make sure they are linked (and people
consulting the issue know you are working on it);
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3. 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;
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4. Make sure existing tests pass;
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5. Add high-coverage tests. No quality testing = no merge.
- If you are adding a new model, make sure that you use
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`ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)` , which triggers the common tests.
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- If you are adding new `@slow` tests, make sure they pass using
`RUN_SLOW=1 python -m pytest tests/test_my_new_model.py` .
- If you are adding a new tokenizer, write tests, and make sure
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`RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py` passes.
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CircleCI does not run the slow tests, but github actions does every night!
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6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_bert.py` for an
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example.
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7. Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
the ones hosted on [`hf-internal-testing` ](https://huggingface.co/hf-internal-testing ) in which to place these files and reference
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them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images ](https://huggingface.co/datasets/huggingface/documentation-images ).
If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
to this dataset.
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See more about the checks run on a pull request in our [PR guide ](pr_checks )
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### Tests
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An extensive test suite is included to test the library behavior and several examples. Library tests can be found in
the [tests folder ](https://github.com/huggingface/transformers/tree/master/tests ) and examples tests in the
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[examples folder ](https://github.com/huggingface/transformers/tree/master/examples ).
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We like `pytest` and `pytest-xdist` because it's faster. From the root of the
repository, here's how to run tests with `pytest` for the library:
```bash
$ python -m pytest -n auto --dist=loadfile -s -v ./tests/
```
and for the examples:
```bash
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$ pip install -r examples/xxx/requirements.txt # only needed the first time
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$ python -m pytest -n auto --dist=loadfile -s -v ./examples/
```
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In fact, that's how `make test` and `make test-examples` are implemented (sans the `pip install` line)!
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You can specify a smaller set of tests in order to test only the feature
you're working on.
By default, slow tests are skipped. Set the `RUN_SLOW` environment variable to
`yes` to run them. This will download many gigabytes of models — make sure you
have enough disk space and a good Internet connection, or a lot of patience!
```bash
$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/
$ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/
```
Likewise, set the `RUN_CUSTOM_TOKENIZERS` environment variable to `yes` to run
tests for custom tokenizers, which don't run by default either.
🤗 Transformers uses `pytest` as a test runner only. It doesn't use any
`pytest` -specific features in the test suite itself.
This means `unittest` is fully supported. Here's how to run tests with
`unittest` :
```bash
$ python -m unittest discover -s tests -t . -v
$ python -m unittest discover -s examples -t examples -v
```
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### Style guide
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For documentation strings, 🤗 Transformers follows the [google style ](https://google.github.io/styleguide/pyguide.html ).
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Check our [documentation writing guide ](https://github.com/huggingface/transformers/tree/master/docs#writing-documentation---specification )
for more information.
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#### This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md)
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### Develop on Windows
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On windows, you need to configure git to transform Windows `CRLF` line endings to Linux `LF` line endings:
`git config core.autocrlf input`
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One way one can run the make command on Window is to pass by MSYS2:
1. [Download MSYS2 ](https://www.msys2.org/ ), we assume to have it installed in C:\msys64
2. Open the command line C:\msys64\msys2.exe (it should be available from the start menu)
3. Run in the shell: `pacman -Syu` and install make with `pacman -S make`
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4. Add `C:\msys64\usr\bin` to your PATH environment variable.
You can now use `make` from any terminal (Powershell, cmd.exe, etc) 🎉
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### Syncing forked master with upstream (HuggingFace) master
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To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs,
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when syncing the master branch of a forked repository, please, follow these steps:
1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead merge directly into the forked master.
2. If a PR is absolutely necessary, use the following steps after checking out your branch:
```
$ git checkout -b your-branch-for-syncing
$ git pull --squash --no-commit upstream master
$ git commit -m '< your message without GitHub references > '
$ git push --set-upstream origin your-branch-for-syncing
```