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Contributing to RandKV

RandKV is intentionally narrow: prompt-protected random KV-cache eviction with small, explicit adapters for inference frameworks.

Before starting a change:

  1. Search open issues and pull requests for overlapping work.
  2. Open an issue before implementing a new backend or model family.
  3. Keep performance claims reproducible and separate from paper-reported data.

Good starting points are listed under the good first issue label. Comment on the issue before coding; a maintainer will confirm the scope and assign it when possible. Larger backend and evaluation work is tracked in ROADMAP.md.

Create the development environment:

python -m venv .venv
.venv/bin/python -m pip install ".[transformers,test]" build

Some Python 3.13 distributions skip hidden .pth files produced by editable build backends. Use a regular local install as shown above; reinstall after changing package source. CI also installs from the built project rather than editable metadata.

Run the required checks:

.venv/bin/ruff check .
.venv/bin/ruff format --check .
.venv/bin/mypy src
.venv/bin/pytest -q
.venv/bin/python -m build --no-isolation

Backend changes must include tests for prompt retention, deterministic selection, cache bounds, and unsupported input behavior. GPU optimizations must retain a tested PyTorch fallback.

Pull requests

  • Keep one pull request focused on one issue.
  • Link the issue with Closes #<number> in the description.
  • Add or update tests for observable behavior changes.
  • Do not include generated benchmark numbers without the raw result artifact.
  • Do not claim a speedup from an unsynchronized, single-run, or unmatched test.
  • Explain compatibility breaks and update the supported-scope table in the README when necessary.

Draft pull requests are welcome when an adapter boundary or schema needs early review.