Individual skill
omh-model-finetuning
[omh] Fine-tuning a model on your own data -- SFT, DPO, RLVR or a LoRA adapter: decide first whether prompting or retrieval already closes the gap, choose the method from the data you have, and promote a checkpoint only when it beats the untuned baseline on a held-out eval. Use when the user says: model-finetuning, model finetuning, model fine-tuning, fine-tune a model, fine-tune the model, fine tune a model, fine tune the model, fine-tune.
agent-skills/omh-model-finetuning/SKILL.md
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# Model Finetuning
This is an OMH `model-finetuning` workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
## Why This Exists
`model-finetuning` exists because producing a model had no owner: `inference-serving` serves one that exists and `llm-app-dev` builds on top of one, while an SFT or DPO question had no baseline comparison and no way to answer that training is not needed.
## First Steps
- Ask what the untuned model and the best prompt score on the held-out examples before discussing any method.
- Ask what shape the data has -- demonstrations, preference pairs, or answers a program can check.
## Do Not Use When