Finetuning
Skill microsoft/skills/.github/plugins/azure-skills/skills/microsoft-foundry/finetuning
Skills, MCP servers, Custom Agents, Agents.md for SDKs to ground Coding Agents
npx -y skills add microsoft/skills --skill finetuningAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
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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).
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
5.4 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it
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 |