A direct answer to the fine-tuning question: Jev itself cannot be fine-tuned, but builders can train or adapt separate decision models, distil labels, or place learned routers around the API.
Original by Made with JevEvaluationMade with Jev guideSource reviewed
Before you dive in
What you’ll find in the original
Jev’s hosted weights and training interface are not public, so there is no supported Jev fine-tuning path.
Open projects instead train small classifiers or adapt base models to emit typed decisions in one forward pass.
Before training, compare the operational value of a custom model against prompting Jev with clearer state, options, and thresholds.
Worth knowing
The alternative-model results are self-reported on different datasets and hardware; they do not establish parity with Jev or calibrated confidence.