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Contributing
Kritarth-Dandapat edited this page May 24, 2026
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Thank you for helping improve BitForge. This project values reproducible measurements and clear documentation over hype.
| 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 |
- Read Architecture for module boundaries and current implementation status
- Check open issues or open one to discuss larger changes
- For org coordination, see the Inference Foundry members list
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- 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.metricsover duplicating metric code - Minimize scope—focused PRs are easier to review
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.
- 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
BitForge is released under the MIT License (see repository license file when published).
- GitHub Issues: Inference-Foundry/BitForge
- Org-wide: Inference-Foundry/.github
-
Maintainers: coordinate via
.github-private(org members)