What it is
Simple Jev lets you use compatible open models from Hugging Face for structured classification and scoring, without training a separate classifier head. It is for developers who want typed decisions from a model's next-token logits.
How it uses Jev
The server reads the model's next-token logits for each question and builds a JSON response with choices, rubric scores, or truth/support judgments. The model does not generate a JSON completion; the server constructs the response from the scores. Shared request validation, versioned prompt instructions, and response scoring live in the plain Python common/ folder.
Primitives:choicescorenoul
Technique worth stealing
Read next-token logits for each question and construct typed JSON responses without generation.
Try it
Use the public demo API with no login, or run the HF server locally with Python 3.12+.
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-centricyes0.93
- Shows a System One patternyes0.83
- Handles uncertaintyno0.05
- Measuredno0.05
- Runnableyes0.91
- Worth recommendingunclear0.40
- Model replicayes0.66
- Problem scopescore on a 0–2 scale1.08
- About Jevyes0.92
Signals by Jev jev-1.13.0, card written by DeepSeek V4.1 Flash from the README on 20 Sept 2026.