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.
Tasks: 代码开发与重构, 测试与调试, 后端与 API, 部署、CI 与发布, 数据与表格分析, 自动化与系统集成, 金融与区块链分析, 规划、决策与项目管理, Git、PR 与团队协作, 安全与维护治理
Creator Heat: 34.2; commercial use: commercial_allowed.