Contributing

We welcome contributions to the LLM Samplers project! Here’s how you can help:

Development Setup

1. Fork the repository on GitHub.

2. Clone your fork locally:

   git clone https://github.com/your-username/llm-samplers.git
   cd llm-samplers
   

3. Create a virtual environment and install development dependencies:

   python -m venv .venv
   source .venv/bin/activate  # On Unix/macOS
   # or
   .venv\Scripts\activate  # On Windows
   
   pip install -e ".[dev]"
   

4. Set up pre-commit hooks:

   pre-commit install
   

Code Style

This project uses Ruff for linting and formatting. Before submitting a pull request:

# Check for issues
ruff check .

# Format your code
ruff format .

All code should follow the project’s style guidelines, which are enforced by Ruff.

Writing Tests

We use pytest for testing. Please include tests for any new features or bug fixes:

1. Add test files in the tests/ directory

2. Run the tests:

   python -m pytest tests/
   
   # For more verbose output
   python -m pytest tests/ -v
   

Pull Request Process

1. Update the documentation if necessary.

2. Make sure all tests pass.

3. Update the README.md if needed.

4. Submit a pull request with a clear description of the changes.

Creating a New Sampler

If you’re implementing a new sampling technique:

1. Create a new file in src/llm_samplers/ named after your sampler (e.g., my_sampler.py)

2. Extend the base Sampler class from base.py

3. Implement the required methods: - __init__: Initialize your sampler with appropriate parameters - adjust_logits: The main method that modifies the logits - Any additional helper methods you need

4. Add your sampler to __init__.py

5. Create tests in the tests/ directory

6. Update documentation to include your new sampler

Documentation

To build the documentation locally:

cd docs
pip install -r requirements.txt
sphinx-build -b html source _build/html

Then open _build/html/index.html in your browser to view the documentation.