/finetuning

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](workflows/quickstart.md) | | **Full pipeline** | [workflows/full-pipeline.md](workflows/full-pipeline.md) | | **Create data** | [workflows/dataset-creation.md](workflows/dataset-creation.md) | | **Iterate** | [workflows/iterative-training.md](workflows/iterative-training.md) | | **Diagnose** | [workflows/diagnose-poor-results.md](workflows/diagnose-poor-results.md) |

References

| Topic | File | |-------|------| | SFT vs DPO vs RFT | [references/training-types.md](references/training-types.md) | | Hyperparameters | [references/hyperparameters.md](references/hyperparameters.md) | | Data formats | [references/dataset-formats.md](references/dataset-formats.md) | | Grader design (RFT) | [references/grader-design.md](references/grader-design.md) | | Reward hacking | [references/reward-hacking.md](references/reward-hacking.md) | | Agentic RFT (tools) | [references/agentic-rft.md](references/agentic-rft.md) | | Deployment | [references/deployment.md](references/deployment.md) | | Training curves | [references/training-curves.md](references/training-curves.md) | | Evaluation | [references/evaluation.md](references/evaluation.md) | | Vision fine-tuning | [references/vision-fine-tuning.md](references/vision-fine-tuning.md) | | Large file uploads | [references/large-file-uploads.md](references/large-file-uploads.md) | | Platform gotchas | [references/platform-gotchas.md](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 |