Fine-Tuning on Azure AI 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).