Contributing

Contributions are welcome! Here's how to get started.

Development Setup

1. Fork and clone:

git clone https://github.com/YOUR_USERNAME/mono-quant.git
cd mono-quant

1. Install in editable mode:

pip install -e ".[dev]"

1. Run tests:

pytest

1. Build documentation:

mkdocs serve

Visit http://localhost:8000 to preview docs.

Code Style

• Follow PEP 8 guidelines
• Use type hints where appropriate
• Add docstrings (Google style) to public functions
• Keep functions focused and small

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=mono_quant --cov-report=html

# Run specific test file
pytest tests/test_quantizers.py

# Run specific test
pytest tests/test_quantizers.py::test_dynamic_quantize

Building Documentation

# Serve locally (live reload)
mkdocs serve

# Build static site
mkdocs build

# Deploy to GitHub Pages
mkdocs gh-deploy

Submitting Changes

1. Create a branch: git checkout -b feature/your-feature
2. Make changes and commit
3. Push to fork: git push origin feature/your-feature
4. Create pull request

Pull Request Guidelines

• Title: Use conventional commits (feat:, fix:, docs:, etc.)
• Description: Explain what and why
• Tests: Include tests for new features
• Docs: Update documentation for API changes

Areas for Contribution

High Priority

• Additional observer types (Entropy, KL-divergence variants)
• ONNX export support
• More calibration strategies
• Benchmarks and performance comparisons

Medium Priority

• TFLite export support
• Model zoo integration examples
• Jupyter notebook tutorials
• Video tutorials

Low Priority

• Quantization-aware training (QAT)
• Automatic layer sensitivity analysis
• Mixed precision quantization
• LLM.int8() style outlier detection

Questions?

Open an issue on GitHub for: - Bug reports - Feature requests - Documentation improvements - General questions

Code of Conduct

Be respectful, constructive, and inclusive. We're all here to build something great together.