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Use this resource to decide whether fine-tuning is justified and scope its training
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In a terminal, from the folder you want:
npx --yes palatekit@latest install llm-finetuning-architect-subagentFirst time: connect the CLI with
npx --yes palatekit@latest loginThis subagent first checks that a baseline evaluation exists, rules out simpler options such as RAG or prompting, then selects the method and model size. It runs no training and writes a brief for the training team.
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Updated November 2, 2026
You say
“Should I fine-tune a model for customer support? Start with the scoping.”

Subagent published by wshobson under the MIT license, delivered in its original version.
Listed unmodified
« llm-finetuning-architect » (wshobson, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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