version: "1.0.0" name: finetuning description: "Fine-tune models on Azure AI 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)." license: MIT metadata: author: Microsoft version: "0.0.0-placeholder"

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).

Workflows

References

Scripts

Rules

1. Always baseline first — evaluate the base model before fine-tuning
2. Validate data before submitting — run scripts/validate/validate_sft.py
3. Calibrate RFT graders — target 25-50% failure rate on the base model
4. Evaluate checkpoints — don't blindly deploy the final one
5. Measure token cost alongside accuracy when comparing models

Quick Reference

Error Handling