Finetuning

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

| 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 |

References

| Topic | File |

|-------|------|

| SFT vs DPO vs RFT | references/training-types.md |

| Hyperparameters | references/hyperparameters.md |

| Data formats | references/dataset-formats.md |

| Grader design (RFT) | references/grader-design.md |

| Reward hacking | references/reward-hacking.md |

| Agentic RFT (tools) | references/agentic-rft.md |

| Deployment | references/deployment.md |

| Training curves | references/training-curves.md |

| Evaluation | references/evaluation.md |

| Vision fine-tuning | references/vision-fine-tuning.md |

| Large file uploads | references/large-file-uploads.md |

| Platform gotchas | references/platform-gotchas.md |

Scripts

| Script | Purpose |

|--------|---------|

| scripts/submit_training.py | Submit SFT/DPO/RFT jobs |

| scripts/monitor_training.py | Poll job until completion |

| scripts/calibrate_grader.py | Find optimal RFT pass_threshold |

| scripts/check_training.py | Analyze curves, list checkpoints |

| scripts/deploy_model.py | Deploy via ARM REST API |

| scripts/evaluate_model.py | LLM judge evaluation |

| scripts/convert_dataset.py | Convert between SFT/DPO/RFT formats |

| scripts/generate_distillation_data.py | Generate synthetic training data |

| scripts/score_dataset.py | Quality scoring on training data |

| scripts/cleanup.py | Delete old files and deployments |

| scripts/validate/ | Data validators (SFT, DPO, RFT) + stats |

Rules

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

Quick Reference

| Task | Command |

|------|---------|

| Validate SFT data | python scripts/validate/validate_sft.py data.jsonl |

| Submit SFT job | python scripts/submit_training.py --model gpt-4.1-mini --training-file train.jsonl --validation-file val.jsonl --type sft |

| Monitor job | python scripts/monitor_training.py --job-id ftjob-xxx |

| Analyze curves | python scripts/check_training.py --job-id ftjob-xxx |

| Deploy model | python scripts/deploy_model.py --model-id ft:gpt-4.1-mini:... --name my-eval |

| Evaluate model | python scripts/evaluate_model.py --deployment-name my-eval --test-file test.jsonl |

Error Handling

| Error | Cause | Fix |

|-------|-------|-----|

| "API version not supported" | Older openai SDK on /v1/ endpoint | Upgrade to openai>=1.0 |

| "does not support fine-tuning with Standard TrainingType" | OSS model needs globalStandard | Use --use-rest flag or script auto-falls back |

| Job stuck in post-training eval | Under-provisioned tool endpoint (RFT) | Scale to S2+, enable Always On |

| "DeploymentNotReady" after ARM succeeds | ARM/data-plane race condition | Delete and recreate deployment, wait 5 min |

| Content safety block at deployment | PII-dense training data | Remove problematic document types |