Fine-Tuning in AI Foundry

This repository contains 12 end-to-end demos and sample datasets for fine-tuning models on Azure AI Foundry. Use this repo to explore practical fine-tuning workflows and access ready-to-use data for your own projects.

📋 Table of Contents

• Quick Start
• Demos
• Sample Datasets
• AI Agent Skills
• Prerequisites
• Contributing

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🚀 Quick Start

New to fine-tuning? Start here:

1. SFT_CNN_DailyMail - Best first demo! Learn Supervised Fine-Tuning with news summarization
2. Sample_Datasets - Understand data formats for SFT, DPO, and RFT

Want an AI coding assistant to guide you?

• Open this repo in VS Code with Copilot, or use Claude/Codex — the agent skill auto-discovers and walks you through the full workflow
• Or follow Skills/workflows/quickstart.md — fine-tune your first model in 6 steps (no demo notebook needed)

Ready for advanced techniques?

• DPO_Intel_Orca - Direct Preference Optimization
• RFT_Countdown - Reinforcement Fine-Tuning

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🎯 Demos

Explore end-to-end fine-tuning experiences in the Demos folder:

Demo	Technique	Use Case	Difficulty
SFT_CNN_DailyMail	SFT	News summarization	⭐ Beginner
SFT_PubMed_Summarization	SFT	Medical paper summarization	⭐ Beginner
DPO_Intel_Orca	DPO	Preference optimization	⭐⭐ Intermediate
RFT_Countdown	RFT	Math puzzle solving	⭐⭐ Intermediate
DistillingSarcasm	Distillation	Knowledge transfer	⭐⭐ Intermediate
Image_Breed_Classification_FT	Vision SFT	Dog breed classification	⭐⭐ Intermediate
Image_FT_Chart_Analysis	Vision SFT	Chart understanding	⭐⭐ Intermediate
Video_FT_Action_Recognition	Vision SFT	Video action detection	⭐⭐⭐ Advanced
ZavaRetailAgent	SFT + RFT	Retail customer service agent	⭐⭐⭐ Advanced
Agentic_RFT_PrivatePreview	RFT	Agentic workflows with tools	⭐⭐⭐ Advanced
Evaluation	Evaluation	Multimodal model evaluation	⭐⭐ Intermediate

👉 See Demos/README.md for detailed descriptions of each demo.

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📊 Sample Datasets

Ready-to-use datasets for testing fine-tuning techniques in the Sample_Datasets folder:

Technique	Dataset	Description
SFT	Text-GSM8K	Grade school math problems
SFT	Multimodal-chartqa	Chart interpretation
SFT	Tool-Calling	Function calling patterns
DPO	orca_dpo_pairs	Preference alignment
RFT	ClauseMatching	Legal contract analysis
RFT	MedMCQ	Medical Q&A

👉 See Sample_Datasets/README.md for data format details and when to use each technique.

⚠️ Note: These datasets are for learning and experimentation only—not for production use. Training jobs may incur costs on your Azure subscription.

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🤖 AI Agent Skills

This repo includes a fine-tuning skill that coding agents can auto-discover and use to help you submit, monitor, and evaluate fine-tuning jobs.

Agent	Skill Path	Auto-discovery
GitHub Copilot (VS Code / CLI)	.github/skills/azure-ai-fine-tuning	✅ Automatic
Claude Code	.claude/skills/azure-ai-fine-tuning	✅ Automatic
Codex / other agents	.agents/skills/azure-ai-fine-tuning	✅ Automatic

All three paths are symlinks to the canonical skill at Skills/, which includes:

• SKILL.md — Agent instructions covering SFT, DPO, and RFT workflows
• 12 scripts — submit, monitor, calibrate, check, deploy, evaluate, validate, score, convert, generate, cleanup, and shared utilities
• 14 reference docs — grader design, hyperparameters, dataset formats, agentic RFT, cost management, and more
• 6 guided workflows — quickstart, full pipeline, dataset creation, iterative training, diagnosis, experiment review
• Sample data — SFT, DPO, and RFT example JSONL files

Using with GitHub Copilot (VS Code)

1. Open this repo in VS Code with Copilot Chat enabled.
2. Ask a fine-tuning task (e.g., "help me submit an SFT job with my dataset").
3. Copilot auto-discovers the skill from .github/skills/ and follows the workflow.

Using with Copilot CLI

cd /path/to/this/repo
copilot
# Then ask: "Submit an SFT fine-tuning job with my training data"

Using with Claude Code

cd /path/to/this/repo
claude
# Then ask: "Fine-tune gpt-4.1-mini on my dataset"

Scripts support uv for zero-setup execution (PEP 723 inline dependencies):

uv run Skills/scripts/submit_training.py --help

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✅ Prerequisites

Before running any demo, ensure you have:

• Azure subscription with access to Azure AI Foundry
• Python 3.9+ installed
• Jupyter Notebook or VS Code with Jupyter extension
• Required Azure role assignments (see individual demo READMEs)

Each demo includes a requirements.txt and .env.template for setup.

👉 New here? See the Getting Started Guide for step-by-step setup instructions.

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Contributing

See CONTRIBUTING.md for guidelines on submitting issues and pull requests.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.