finetuning

Fine-tune models on Azure AI Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job…

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