Finetuning Method Selection

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Decide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fine-tuning effort, when unsure whether RAG or prompting would suffice, or when choosing between preference-optimization and reinforcement methods.

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SKILL.md

finetuning-method-selection

Fine-Tuning Method Selection

This is the router skill for the fine-tuning lifecycle: it decides whether fine-tuning is the right tool at all, and if so, which method and which base-model size class. Every other skill in this plugin assumes this routing already happened — start here before opening lora-qlora-recipes, preference-optimization, or grpo-rlvr-training.

When to Use This Skill

Starting any fine-tuning effort, before a framework or base model has been chosen.
Unsure whether RAG or prompt engineering would solve the problem more cheaply than training.
Choosing between preference optimization (DPO family) and a reinforcement method (GRPO/RLVR) for the same underlying task.
Sizing a candidate model/method combination before committing to a run.

3 files · 16 KB8 KB

Version history

Situation	Route
Facts change often (prices, docs, news)	RAG, not fine-tuning
Desired behavior still being figured out	Prompt engineering
Stable domain knowledge, ≥500MB text	CPT then SFT — see Off-Ramps First
Have input/output demonstrations	SFT — see `lora-qlora-recipes`
Have preference pairs or thumbs-up/down	DPO/ORPO/KTO — see `preference-optimization`
Have a verifiable pass/fail signal	GRPO+RLVR — see `grpo-rlvr-training`
No eval harness yet	Stop — see `eval-harness-first`

Domain text volume	Route
<10MB	RAG only
10MB–500MB	RAG + fine-tune
500MB–10GB	CPT, then SFT
>10GB	CPT required

Finetuning Method Selection

Fine-Tuning Method Selection

When to Use This Skill

Quick Reference

Off-Ramps First

Method Router

Worked Routing Examples

Key Routing Facts

Common Routing Mistakes

Model Selection

Memory Feasibility

Related Skills