{
 "schema_version": 1,
 "generated": "2026-10-26",
 "id": "jaredpalmer-kev--fine-tune-kev-on-your-labels",
 "repo": "jaredpalmer/kev",
 "repo_url": "https://github.com/jaredpalmer/kev",
 "repo_slug": "jaredpalmer-kev",
 "title": "Fine-tune Kev on your own support tickets with an agent skill",
 "page_title": "How to fine-tune Kev on your own support tickets with an agent skill with Kev",
 "summary": "Install the kev-finetune skill and ask your agent to fine-tune Kev on your data.",
 "who": "An ML-curious developer with labeled tickets or complaints whose zero-shot accuracy is too low.",
 "problem": "Generic models do not know your routing categories or escalation rules.",
 "outcome": "A fine-tuned Kev with a temperature fitted to your labels, served from your own endpoint.",
 "steps": [
  "Run `npx skills add jaredpalmer/kev@kev-finetune`",
  "Ask your agent to fine-tune Kev on your support tickets",
  "Let the skill interview you and find the questions your code already asks",
  "Train on Modal, check accuracy on held-out data and size your dataset first",
  "Deploy the result as your own HTTPS endpoint that scales to zero"
 ],
 "agent_prompt": "Use the kev-finetune skill from jaredpalmer/kev to fine-tune Kev-4B on my labeled tickets in <file>. Report accuracy before and after on a held-out split, and stop before deploying if the gain is within noise.",
 "agents": [
  "claude-code",
  "codex"
 ],
 "difficulty": "Advanced",
 "time": "half a day",
 "basis": "readme",
 "caveat": "The README notes that with about 400 records the gain was inside the noise, so size your dataset before training.",
 "category": "Work and operations",
 "tags": [
  "Work and operations"
 ],
 "lang": "python",
 "requirements": [
  "Python with uv; install from source (pip install kev is a different package)",
  "A GPU (CUDA) or an Apple Silicon Mac with MLX; Kev-0.8B runs on a laptop",
  "Optional: a Modal account for fine-tuning and hosted endpoints"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/jaredpalmer-kev/fine-tune-kev-on-your-labels/",
 "md": "https://dedupedai.com/use-cases/jaredpalmer-kev/fine-tune-kev-on-your-labels/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/jaredpalmer-kev--fine-tune-kev-on-your-labels.json",
 "api_version": "v1"
}