When should I choose fine-tuning over RAG?

Choose fine-tuning for deep specialization, specific output styles, or format control where prompt engineering and RAG have failed to capture necessary nuances or domain-specific styles.

What is the difference between LoRA and QLoRA?

LoRA uses low-rank adapters to update weights efficiently, while QLoRA adds 4-bit quantization to drastically reduce memory usage, allowing large models to be trained on consumer-grade GPUs.

Does this skill support Unsloth?

Yes, it includes implementation patterns for Unsloth, providing up to 2x faster training speeds and significantly reduced VRAM usage compared to standard transformers methods.

How many examples do I need for effective fine-tuning?

For most domain-specific tasks, it is generally recommended to have at least 1,000 high-quality training examples to ensure the model captures the desired patterns without overfitting.

What is DPO alignment used for?

Direct Preference Optimization (DPO) is used to align model behavior with human preferences by training on a dataset of 'chosen' and 'rejected' responses to the same prompt.

Fine-Tuning & Customization

Name: Fine-Tuning & Customization
Author: yonatangross

byyonatangross

•

データサイエンスとML

Simplifies Large Language Model fine-tuning and alignment using parameter-efficient techniques like LoRA, QLoRA, and DPO.

This skill provides a comprehensive framework for customizing Large Language Models (LLMs) to specific domains or personas. It guides users through the critical decision-making process of when to fine-tune versus using RAG or prompting, and provides production-ready code patterns for LoRA, QLoRA, and Direct Preference Optimization (DPO). By leveraging industry-leading tools like Unsloth and TRL, the skill enables memory-efficient training on consumer hardware and includes sophisticated methods for synthetic data generation to improve model performance and ensure strict format consistency.

主な機能

01Synthetic training data generation using teacher-student distillation patterns

02Parameter-efficient fine-tuning with LoRA and QLoRA support

03Detailed decision frameworks for evaluating fine-tuning vs. RAG vs. Prompting

04Memory-optimized training configurations via Unsloth integration

05Direct Preference Optimization (DPO) for human-aligned model outputs

0669 GitHub stars

ユースケース

01Adapting models to follow strict industry-specific formatting or technical jargon

02Embedding a specific brand voice or unique persona deeply into an LLM's identity

03Training small, efficient models for niche tasks using high-quality synthetic data

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