llm-zipper 0.0.1

Know before you compress. LLM-zipper is an open-source framework for cheaper, faster, energy-efficient LLMs. It predicts how much a model shrinks, and at what quality cost, before you spend the compute.

We are AIDA (AI in Dynamic Action), a research group led by Dr. habil. Pavel Osinenko. We work on making large models cheaper and faster to run:

• KV-cache compression
• LLM acceleration
• memory-efficient RAG
• RL for adaptive inference
• fine-tuning, tensorization and more

New LLM-zipper modules arrive together with our papers, so each one brings both the theory and working code.

_{Our goal: take LLMs from data-center scale down to a rig you can run at home.}

Install

pip install llm-zipper
llm-zipper --version

Roadmap

LLM-zipper grows one module at a time. Every module follows the same contract: scan predicts how compressible a part of the model is, compress applies a method, and evaluate measures what it cost.

Development

git clone https://github.com/aidagroup/LLM-zipper.git && cd LLM-zipper
uv sync                     # package + dev tools
uv run pre-commit install   # lint and format on every commit
uv run pytest

To release, bump __version__ in src/llm_zipper/__init__.py, merge to main, then push a matching tag such as v0.1.0. GitHub Actions builds the package and publishes it to PyPI.

Citation

If LLM-zipper helps your research, please cite it with the metadata in CITATION.cff. On GitHub, use Cite this repository.

License

Apache-2.0