Who it is forAn ML engineer whose zero-shot routing accuracy is not good enough for production.
The problemShipped checkpoints work zero-shot, but domain specific decisions need domain data to be reliable.
The outcomeA fine-tuned checkpoint trained with RLCD, calibrated and evaluated on your workflows.
What you need first
- 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
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
Prompt for your agent
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.
Caveat. The README's accuracy jump (0.362 to 0.766) is on its own typed-decisions benchmark; your gain depends on your data.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for NandhaKishorM/laya and "Fine-tune Laya on your own decisions for higher accuracy"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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