What it is
AnyJev is a Python library that turns any open transformers or vLLM model into a Jev-style decision model. It lets you ask typed questions (Choice/Score/Noul) and get calibrated probabilities from one prefill, with no training. It is for developers who need reliable, thresholdable decisions from LLMs.
How it uses Jev
AnyJev implements Jev-style decisions by reading the next-token distribution from one prefill. It uses cyclic-shift marginalization and a label-free prior estimate to reduce option-order sensitivity. Results are labeled raw, L0, or L1 based on debiasing and calibration. L2 uses a closed-form head on hidden states for better accuracy.
Primitives:choicescorenoul
Technique worth stealing
Cyclic-shift marginalization and label-free prior estimate reduce option-order sensitivity without labels.
Try it
pip install "anyjev[hf]" then run python -m demo.jev_mode --backend fake
Evidence
Each line is one question put to Jev about the README. ≥ 0.60 reads as yes, ≤ 0.40 as no; in between Jev is not making a call.
- Jev-centricyes0.95
- Shows a System One patternno0.33
- Handles uncertaintyno0.07
- Measuredno0.30
- Runnableyes0.86
- Worth recommendingunclear0.58
- Model replicayes0.90
- Problem scopescore on a 0–2 scale1.29
- About Jevyes0.94
Signals by Jev jev-1.13.0, card written by DeepSeek V4.1 Flash from the README on 23 Sept 2026.