finetuning-method-selection
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.
Profile →npx skills add wshobson/agents --skill finetuning-method-selection --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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. ## Quick Reference | 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`
- 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
What does the finetuning-method-selection skill do?
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.
How do I install it?
Run `npx skills add wshobson/agents --skill finetuning-method-selection --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From wshobson/agents, a repository with 38,479 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.