# AI Finetuning

> Practical knowledge for finetuning foundation models. Covers when to finetune (vs prompt engineering or RAG), memory bottlenecks (backpropagation, quantization, numerical representations), parameter-efficient finetuning techniques (PEFT, LoRA, adapters), model merging strategies (summing, layer stacking, concatenation), and finetuning tactics (frameworks, hyperparameters). Use this skill when: - Deciding whether to finetune (vs prompt engineering or RAG) - Estimating memory requirements for finetuning - Implementing LoRA or other PEFT techniques - Merging multiple finetuned models - Choosing finetuning hyperparameters or frameworks - Quantizing models for inference or training

- Skill: `ebarti/ai-finetuning` (Agent Skill, multi-file: 24 files)
- Install (CLI): `npx skillmds@latest add ebarti/ai-finetuning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ebarti/ai-finetuning/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: ebarti (https://skillmd.com/u/ebarti)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/ebarti/ai-finetuning

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# AI Finetuning

Knowledge from "AI Engineering" by Chip Huyen (Chapter 7). Practical guide to model finetuning with focus on parameter-efficient methods.

## Quick Start

1. Check `guidelines.md` to find which files to load for your task
2. Load only relevant files (each topic has knowledge.md, rules.md, examples.md)
3. Apply guidance to your work

## Contents

### References

| Category | Purpose |
|----------|---------|
| `finetuning-overview` | When to finetune, reasons for/against, finetuning vs RAG |
| `memory-bottlenecks` | Backpropagation memory, numerical representations, quantization |
| `peft-techniques` | Parameter-efficient finetuning, LoRA, adapter methods |
| `model-merging` | Summing, layer stacking, concatenation for multi-task models |
| `finetuning-tactics` | Frameworks, base model selection, hyperparameters |

### Workflows

| Task | Workflow |
|------|----------|
| Decide whether to finetune | `workflows/should-i-finetune.md` |
| Set up a finetuning job (memory → method → params) | `workflows/setup-finetuning.md` |

## Guidelines

See `guidelines.md` for task-based file selection.