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name: finetuning 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)." license: MIT metadata: author: Microsoft version: "0.0.0-placeholder"

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 | | 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

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

| 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 |