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Contributing

Kritarth-Dandapat edited this page May 24, 2026 · 1 revision

Contributing

Thank you for helping improve BitForge. This project values reproducible measurements and clear documentation over hype.

Ways to contribute

Area Examples
Core library Finish GPTQ/AWQ calibration loops, GGUF export
Experiments Wire real model loads, add datasets, export CSV/JSON
Benchmarks CI-friendly regression suite on a tiny reference model
Docs Wiki updates, worked examples, hardware-specific notes
Interactive lab Connect simulator to real benchmark exports

Before you start

  1. Read Architecture for module boundaries and current implementation status
  2. Check open issues or open one to discuss larger changes
  3. For org coordination, see the Inference Foundry members list

Development setup

git clone https://github.com/Inference-Foundry/BitForge.git
cd BitForge
pip install -e .

Run experiment smoke tests:

python experiments/01_perplexity_benchmarks.py
python experiments/04_outlier_detection.py

Code conventions

  • Match existing patterns: dataclasses for config, module docstrings, skeletal methods clearly marked
  • Keep experiment scripts runnable with python experiments/NN_*.py
  • Prefer extending bitforge.evaluation.metrics over duplicating metric code
  • Minimize scope—focused PRs are easier to review

Useful background

Contributors often have experience in:

  • Information theory and linear algebra (quantization error analysis)
  • PyTorch and the Hugging Face ecosystem
  • llama.cpp / GGUF tooling
  • Running LLMs on consumer GPUs and Apple Silicon

You don't need all of these—documentation and experiment harness improvements are equally valuable.

Pull request checklist

  • Changes are scoped to one concern
  • Existing experiment scripts still run
  • Wiki updated if CLI, architecture, or workflows change
  • No secrets or large model weights committed

License

BitForge is released under the MIT License (see repository license file when published).

Questions

Clone this wiki locally