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Contributing to Squeez

Thanks for your interest in contributing! This guide will get you up and running.

Setup

# Clone the repo
git clone https://github.com/KRLabsOrg/squeez.git
cd squeez

# Create a virtual environment (Python 3.10+)
python -m venv .venv
source .venv/bin/activate

# Install in development mode
pip install -e ".[dev]"

Code style

We use Ruff for both linting and formatting, with a line length of 100.

# Check formatting
ruff format --check squeez/ tests/

# Auto-format
ruff format squeez/ tests/

# Lint
ruff check squeez/ tests/

# Lint with auto-fix
ruff check --fix squeez/ tests/

Key conventions:

  • Use modern type hints (list[str], dict[str, Any], str | None)
  • Add docstrings to public classes and methods
  • Use logging instead of print() for runtime messages
  • Use pathlib.Path instead of os.path

Running tests

pytest tests/ -v

Project structure

squeez/
  inference/         # Runtime extractor (CLI + Python API)
    extractor.py      # ToolOutputExtractor with vLLM/transformers backends
  training/          # Model training pipeline
    train.py          # LoRA fine-tuning script
    dataset.py        # SFT dataset with label masking
    evaluate.py       # Evaluation metrics
  data/              # Data generation pipeline
    pipeline.py       # Main pipeline orchestrator
    config.py         # Configuration and system prompt
    swebench_loader.py
    source_fetcher.py
    tool_call_generator.py
    tool_call_executor.py
    auto_labeler.py
    llm_distiller.py
    sample_assembler.py
    validator.py
configs/             # YAML configuration files
scripts/             # Utility scripts
tests/               # Pytest test suite

Making changes

  1. Create a branch from main:

    git checkout -b my-feature

  2. Make your changes. Keep PRs focused — one feature or fix per PR.

  3. Run lint and tests before committing:

    ruff format squeez/ tests/
ruff check squeez/ tests/
pytest tests/ -v

  4. Open a pull request against main. CI will run lint and tests automatically.

What to work on

Good areas for contribution:

  • New tool types — add support for more tool output formats in the data generation pipeline
  • Model backends — add new inference backends (e.g. GGUF, TensorRT)
  • Evaluation — improve metrics or add new evaluation methods
  • Tests — increase coverage
  • Bug fixes — check open issues

License

By contributing, you agree that your contributions will be licensed under the Apache 2.0 License.