finetuning

Topics in the source

• Fine-Tuning on Microsoft Foundry
• When to Use
• Workflows
• References
• Scripts
• Rules
• Quick Reference
• Error Handling

Fine-tune models using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset prep, training, deployment, and evaluation. 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). | Stage | Guide | |-------|-------| | Quick start | workflows/quickstart.md | | Full pipeline | workflows/full-pipeline.md | | Create data | workflows/dataset-creation.md | | Iterate | workflows/iterative-training.md | | Diagnose | workflows/diagnose-poor-results.md | | Topic | File | |-------|------| | SFT vs DPO vs RFT | references/training-types.md | | Hyperparameters | reference

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