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SAEBase v0.2.0 — M0–M5

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SAEBase v0.2.0

The first P0 release of OpenSAE SAEBase. M0–M5 deliver reference checkpoint
acceptance, activation caching, intervention evaluation, multi-metric
benchmarking, statistical reliability auditing and a real checkpoint
release matrix.

What's included

  • Python 3.11 and 3.12 distribution with the original SAEBase API and
    saebase-benchmark, saebase-audit, saebase-ground-truth and
    saebase-release-matrix command-line interfaces.
  • Two verified public language-model families: Pythia-70M deduped and
    GPT-2 Small, two real SAE checkpoints per family at the same hook.
  • Six fixed prompt groups per model, identity and reconstruction conditions,
    verified activation/code sharing, original metric measurements, grouped
    confidence intervals, and hash-linked portable reports.
  • Checkpoint identity SHA-256, execution time and RSS metadata, synthetic
    failure-injection resume tests and a machine-verifiable release gate.
  • No global SAE-quality score, model/SAE weight binaries, or third-party
    evaluation datasets bundled into the package.

Install

Install the wheel attached to this GitHub Release with Python 3.11 or 3.12.
Optional analysis integrations require their separate dependencies. Real
checkpoint reproduction downloads upstream public weights on demand.

python -m pip install opensae_saebase-0.2.0-py3-none-any.whl
saebase-release-matrix --help

The package is under the stated SAEBase Apache-2.0 software license.
Each external model/SAE remains subject to its own upstream license.
Some SAE checkpoint redistribution rights are not independently verified,
so weights are never included here. This is not a license certification.

The four-checkpoint, six-prompt real run validates the engineering pipeline.
It does not establish universal model behavior, population confidence limits,
interpretability quality or a comprehensive benchmark ranking. The optional
extended SAE widths, gated Gemma models, and GPU generation backends are not
covered by this minimum release matrix. No PyPI deployment is implied.

For reproducible protocols, identifiers, provenance and release validation
steps, see M5 release matrix.

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