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15 - Your First Contribution

What this session is

The whole thing. Walk through an AI OSS contribution end-to-end.

The workflow

  1. Fork on GitHub.
  2. Clone your fork.
  3. Add upstream as remote.
  4. Branch off main.
  5. Set up the dev environment (install with extras; run tests).
  6. Change the file(s).
  7. Run lint + tests locally.
  8. Push to your fork; open PR.

Step 1: Fork & clone

git clone git@github.com:<you>/peft.git
cd peft
git remote add upstream git@github.com:huggingface/peft.git
git fetch upstream

Step 2: Branch

git checkout -b docs/fix-lora-example

Always a fresh branch off main.

Step 3: Set up dev environment

For most HF projects:

python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,test]"
pytest tests/ -x -q

For projects requiring GPU, run only CPU tests first:

pytest tests/ -x -q -m "not gpu"

If anything fails on a fresh clone, fix that first or ask in the issue.

Step 4: Make the change

Small. Focused. Tested.

  • Docs typo / clarification - edit the .md file in docs/source/.
  • Add an example - add a new file under examples/.
  • Fix a bug - change the code; add or update a test that proves the fix.

For a first PR, prefer the first two. Bug fixes are great once you know the project.

Step 5: Re-run CI's commands locally

Look in .github/workflows/tests.yml. Typical:

make quality              # ruff / black / isort
make test                 # pytest
make docs                 # sphinx build

All green? Push. Red? Fix locally first.

Step 6: Commit and push

git add <files>
git commit -m "docs: fix LoRA target_modules example for Llama"

DCO if required (git commit -s).

git push origin docs/fix-lora-example

Step 7: Open the PR

On upstream repo, "Compare & pull request."

  • Title. Short, descriptive. Conventional-commit style if the project uses it.
  • Description. What changed, why, how tested. Closes #123 references the issue.
  • Checklist. Address every item in the PR template.

Submit. CI runs. Fix anything red by pushing more commits.

Worked example: typo in PEFT LoRA docs

Suppose you noticed docs/source/conceptual_guides/lora.md has an outdated target_modules=["query_key_value"] example that no longer applies to current Llama configs.

git clone git@github.com:<you>/peft.git
cd peft
git remote add upstream git@github.com:huggingface/peft.git
git fetch upstream

git checkout -b docs/lora-target-modules-llama

# Edit docs/source/conceptual_guides/lora.md
# Add a note: "For Llama-style models, use ['q_proj','v_proj']."

make quality
make docs

git add docs/source/conceptual_guides/lora.md
git commit -m "docs: clarify LoRA target_modules for Llama-style models"
git push origin docs/lora-target-modules-llama

Open PR. Wait for review.

What review looks like

  1. "LGTM, merging." Done.
  2. "Could you change these?" Address. Push commits to same branch.
  3. "Not quite - we already have a section for this." Update or close.
  4. Silence for a week → polite check-in comment.

HF teams are responsive (usually within days).

After the merge

  • Update your fork's main: 
    git checkout main
git fetch upstream
git merge upstream/main
git push origin main
  • Delete the branch.
  • Take a screenshot.
  • Sit with it.

After your first PR

  1. Pick another issue. Familiarity compounds - second is much easier.
  2. After 3-5 PRs in one project, become a regular. Review others' PRs.
  3. Pick a model architecture you care about. Contribute an integration.
  4. Move toward research code: paper implementations, training-script improvements.

What you might wonder

"PR sits for weeks?" HF responds fast. Other AI projects (research orgs, slower-paced labs) can take longer. Polite check-in after 7-10 days.

"What about PyTorch core?" Larger surface, more rigorous review. CLA required, RFCs for non-trivial changes. Start with the docs/ tree there.

"What about OpenAI / Anthropic SDKs?" Yes, they accept PRs to their clients (openai-python, anthropic-sdk-python). Closed-source models, open-source clients.

"Maintainer rude?" Disengage. Try another project. AI OSS has many welcoming homes.

Done with this path

You've: - Installed PyTorch and the AI Python stack. - Trained a small neural net on MNIST. - Used Hugging Face for text generation. - Fine-tuned a model with LoRA. - Built a small RAG pipeline. - Evaluated outputs honestly. - Served a model locally. - Read a real AI OSS project. - Submitted a PR.

What you should do next: build a small AI tool you actually want to exist. The technology rewards practice. Pick one problem, build the simplest possible solution, iterate.

Recommended next paths on this site:

Congratulations. You are no longer a beginner.

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