Ai finetuning
📚 Agent skills distilled from technical books — AI Engineering, Context Engineering, Designing Data-Intensive Applications, and more. Agent-agnostic, plain Markdown. Give your AI agent a bookshelf.
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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.md
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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
- Check
guidelines.mdto find which files to load for your task - Load only relevant files (each topic has knowledge.md, rules.md, examples.md)
- 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.