aionedge.AnyJev
Training-free Jev-style typed decisions from LLM next-token probabilities. L0 uses cyclic option rotations and label-prior correction to reduce bias without labels; L1 fits temperature calibration using labeled examples. No base-model fine-tuning; calibration and order stability are not universal guarantees.
- GitHub stars
- 23 as of 2026-09-22
- Forks
- 86
- Language
- Python
- License
- Apache-2.0
- Created
- 2026-09-21
- Last updated on GitHub
- 2026-09-25
What Jev does in AnyJev
Choice, Noul, and Score primitives over an underlying LLM; not the hosted TypeSafe Jev model.
Verification
Verified. We confirmed this repository through the GitHub API and located the code that calls Jev at a pinned commit: view the Jev code ↗. Upstream code changes over time, so check that the file still exists at the current commit before citing it.
Topics
- calibration
- decision-model
- jev
- jev-model
- llm
- system-one
- transformers
- vllm
How Jev-like models projects use Jev
Independent models and servers that reproduce the Jev decision pattern, often on open weights you can run yourself. None of them is the hosted TypeSafe model, but they are useful for offline work, research and teams that cannot send data to an external API.
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Python