{
 "schema_version": 1,
 "generated": "2026-10-26",
 "id": "nandhakishorm-laya--fine-tune-laya-on-your-decisions",
 "repo": "NandhaKishorM/laya",
 "repo_url": "https://github.com/NandhaKishorM/laya",
 "repo_slug": "nandhakishorm-laya",
 "title": "Fine-tune Laya on your own decisions for higher accuracy",
 "page_title": "How to fine-tune Laya on your own decisions for higher accuracy with Laya",
 "summary": "Use the fine-tuning notebook or Apple Silicon script to train on decisions from your domain.",
 "who": "An ML engineer whose zero-shot routing accuracy is not good enough for production.",
 "problem": "Shipped checkpoints work zero-shot, but domain specific decisions need domain data to be reliable.",
 "outcome": "A fine-tuned checkpoint trained with RLCD, calibrated and evaluated on your workflows.",
 "steps": [
  "Collect labeled decisions from your own workflow",
  "Open the Fine-tuning notebook (Kaggle 2xT4) or the Apple Silicon script from the README",
  "Run the whole loop: build the dataset, train with RLCD, calibrate temperatures, evaluate and export",
  "Compare accuracy against the base checkpoint on held-out decisions",
  "Load the exported checkpoint in your Router"
 ],
 "agent_prompt": "Help me fine-tune NandhaKishorM/laya on my labeled decisions in <file> using the README's Fine-tuning notebook flow. Build the dataset, train, calibrate and evaluate, then report accuracy against the base English checkpoint on a held-out split.",
 "agents": [
  "claude-code",
  "codex"
 ],
 "difficulty": "Advanced",
 "time": "half a day",
 "basis": "readme",
 "caveat": "The README's accuracy jump (0.362 to 0.766) is on its own typed-decisions benchmark; your gain depends on your data.",
 "category": "Work and operations",
 "tags": [
  "Work and operations"
 ],
 "lang": "python",
 "requirements": [
  "Python 3.10 or newer",
  "pip or uv",
  "A GPU is optional; checkpoints download on first use",
  "Optional extras: laya[serve], laya[mcp], laya[langchain] and others"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/nandhakishorm-laya/fine-tune-laya-on-your-decisions/",
 "md": "https://dedupedai.com/use-cases/nandhakishorm-laya/fine-tune-laya-on-your-decisions/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/nandhakishorm-laya--fine-tune-laya-on-your-decisions.json",
 "api_version": "v1"
}