LLM Fine-Tuning Guide
$2.99OfficialPlan and execute LLM fine-tuning: dataset preparation, method selection (LoRA, QLoRA, full), evaluation, and deployment.
datallmfine-tuningloraqlora· by SkillingMain
What you get
- ✓9-step procedure
- ✓6 pitfalls to avoid
- ✓Installs into 6 tools
- Version
- v1 →
- Last updated
- today
- Length
- 3 min read
- Requires
- Best with a strong model (Claude Sonnet 4)
Works in: Claude Code, Codex, Cline, opencode, OpenClaw, Hermes · Handles multi-file projects
Preview
When to use
Use this skill when a base LLM underperforms on your domain task and prompting, RAG, and few-shot examples have plateaued. It covers supervised fine-tuning (instruction and chat), parameter-efficient methods (LoRA/QLoRA), and full fine-tuning, plus the dataset prep, evaluation, and deployment around them. Reach for it whenever a behavior change — not just knowledge injection — is required.
Inputs to gather
- Base model, size, and license (open weights vs. proprietary API)
- Task type: instruction following, classification, extraction, code, dialogue
- Training data: size, format, quality, and whether it's human or model-generated
- Hardware budget (GPU count, VRAM, train
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