Agent Skills
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finetuning
GitHub提供在 Microsoft Foundry 上进行模型微调（SFT/DPO/RFT）的完整指南，涵盖数据准备、训练提交、部署及评估流程。
触发场景
进行 SFT、DPO 或 RFT 微调
准备或格式化训练数据集
提交和监控训练作业
部署或评估微调后的模型
安装
npx skills add microsoft/azure-skills --skill finetuning -g -y
SKILL.md
Frontmatter
{
"name": "finetuning",
"license": "MIT",
"metadata": {
"author": "Microsoft",
"version": "0.0.0-placeholder"
},
"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)."
}
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
- 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 |
版本历史
-
ea76537
当前 2026-08-20 03:58
将平台名称从Azure AI Foundry更新为Microsoft Foundry。
- 013b97d 2026-07-25 09:46
