When should I choose fine-tuning over RAG?

Fine-tuning is recommended as a last resort when prompt engineering and RAG fail to capture domain nuances, or when you need to deeply embed a specific persona or output format.

Does this skill support alignment techniques?

Yes, it includes implementation patterns for Direct Preference Optimization (DPO) to align model behavior with specific human preferences and safety guidelines.

What is the benefit of using QLoRA with this skill?

QLoRA allows you to train large models like LLaMA 70B on hardware with less than 48GB VRAM by utilizing 4-bit quantization, saving over 75% of memory.

How much data do I need for effective fine-tuning?

While it varies by task, this skill recommends having at least 1,000 high-quality examples to achieve consistent results for domain specialization.

Fine-Tuning & Customization

Name: Fine-Tuning & Customization
Author: yonatangross

byyonatangross

•

データサイエンスとML

Optimizes Large Language Models for specific domains using parameter-efficient fine-tuning, DPO alignment, and synthetic data generation.

This skill provides a comprehensive framework for customizing LLMs using modern techniques like LoRA and QLoRA via Unsloth, alongside alignment methods like Direct Preference Optimization (DPO). It includes a robust decision-making matrix to determine when fine-tuning is superior to RAG or prompt engineering, and offers tools for generating high-quality synthetic training datasets. Whether you are aiming for deep domain specialization, specific output formatting, or style embedding, this skill streamlines the end-to-end training pipeline while ensuring production-ready efficiency on consumer-grade hardware.

主な機能

0169 GitHub stars

02Strategic decision framework for choosing between RAG, prompting, and fine-tuning

03Model alignment using Direct Preference Optimization (DPO) and RLHF patterns

04Automated synthetic data generation for teacher-student model distillation

05Optimized training configurations for LLaMA-3 and other transformer models

06Efficient fine-tuning using LoRA and QLoRA via the Unsloth framework

ユースケース

01Training high-performance models on memory-constrained consumer GPUs

02Specializing a base model for niche legal, medical, or technical domains

03Standardizing model output formats and personas for consistent enterprise branding

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yonatangross/orchestkit fine-tuning-customization

For use in Claude.ai and ChatGPT

Download Skill