AI & Machine Learning
Kev Finetune
Fine-tune a Kev decision model (open Jev-style System One model) on a user's own workload and serve it with calibrated probabilities. Interviews the user, finds existing Jev/TypeSafe questions or labelled data in their codebase, generates enough synthetic training data to measure a gain, trains and calibrates on Modal, scores against the released checkpoint, deploys a TypeSafe-compatible endpoint, and tears everything down. Use when someone wants noul/choice/score questions answered on their own domain (routing, triage, moderation, classification, scoring), wants calibrated confidence for automation thresholds, mentions Jev, TypeSafe, System One or Kev, or asks to fine-tune, evaluate, deploy or clean up Kev.
- Publisher / source
- jaredpalmer/kev (found on GitHub top repositories)
- Upstream record
- jaredpalmer/kev
- Version
- Version 1
- Published
- Publication review
- Approved for publication
- Content fingerprint
72ce587c7fbdaa5a8392a472f544ae03a007eff5ce89fd66f4b2110e9165a91b