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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

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AI & ML

File contents

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.

ebarti/skills/tree/main/ai-finetuning commit 501a578595

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Frequently asked questions

npx skillmds@latest add ebarti/ai-finetuning