# Finetuning

Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT...

- Repository: https://github.com/microsoft/azure-skills (1609 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-30
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add microsoft/azure-skills --skill finetuning -g -a claude-code -y`
Codex: `npx skills add microsoft/azure-skills --skill finetuning -g -a codex -y`
Cursor: `npx skills add microsoft/azure-skills --skill finetuning -g -a cursor -y`
Gemini CLI: `npx skills add microsoft/azure-skills --skill finetuning -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add microsoft/azure-skills`

## Description

Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Fine-Tuning on Microsoft Foundry

Fine-tune models using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset prep, training, deployment, and evaluation.

## When to Use

Use this sub-skill when the user asks about:
- Fine-tuning a model (SFT, DPO, or RFT)
- Preparing, validating, or formatting training data
- Submitting, monitoring, or diagnosing training jobs
- Calibrating graders or pass thresholds for RFT
- Deploying or evaluating a fine-tuned model
- Choosing between training types (SFT vs DPO vs RFT)
- Distillation, synthetic data generation, or dataset quality scoring
- Large file uploads for training data
- Cleaning up fine-tuning resources (files, deployments)

**Do NOT use for:** General model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).

## Workflows

| Stage | Guide |
|-------|-------|
| **Quick start** | [workflows/quickstart.md](workflows/quickstart.md) |
| **Full pipeline** | [workflows/full-pipeline.md](workflows/full-pipeline.md) |
| **Create data** | [workflows/dataset-creation.md](workflows/dataset-creation.md) |
| **Iterate** | [workflows/iterative-training.md](workflows/iterative-training.md) |
| **Diagnose** | [workflows/diagnose-poor-results.md](workflows/diagnose-poor-results.md) |

## References

The rest: https://github.com/microsoft/azure-skills/blob/HEAD/.github/plugins/azure-skills/skills/microsoft-foundry/finetuning/SKILL.md