What you can build with it

Purpose and audience

This learning project answers choice, yes/no and ordered-score questions about one image by directly scoring candidate outputs.

Evidence 1 ↗

How Jev fits

It is an independent community exploration of Jev-like inference, without reproducing TypeSafe's proprietary architecture, training, calibration or serving system.

Evidence 1 ↗

Using the project

Use an Apple Silicon Mac with Metal, download the model, then analyze images through the local browser interface, CLI or HTTP API.

Evidence 1 ↗

Scope and limits

Candidate probabilities are uncalibrated relative scores. Only the Qwen3.5 adapter is verified; the published benchmark studies scaling, not accuracy or Jev equivalence.

Evidence 1 ↗ · Evidence 2 ↗

Repository description

An educational Jev-like visual inference experiment on Apple Silicon: shared context, direct candidate scoring, and local visual demos.

Project facts

Relationship to Jev
Local alternative
Evidence
Documented
Language
Python
License
MIT
Origin
Original repository
Repository status
Not archived
Created
2026-09-17
Last code update
2026-09-21
GitHub stars
296
Evidence checked
Sep 30, 2026
Metadata checked
Sep 29, 2026
Check status
Up to date

Stars measure the whole repository, including work unrelated to Jev.

Evidence and scope

Documented records the linked documentation or source. JevHunt has not independently run or benchmarked this project.

Evidence commit: 4382bba455647400951429134ceb012ca155e3fe

Discovered through: awesome-jev.

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