Finetuning

MicrosoftOfficialCreated by Microsoft

Skill
Tool integration
Data and AI
MIT
Low risk
Has scripts

From the author

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).

— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides the necessary procedures and tools for fine-tuning AI models on Microsoft Foundry. It supports three primary training methodologies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Fine-Tuning (RFT). The skill encompasses the entire lifecycle, including dataset preparation, training job submission, monitoring, model evaluation, and deployment. It also includes specific guidance for agentic RFT, where models are trained to utilize external tools, and provides scripts for data validation, grader calibration, and resource management.

What it does

Supports SFT, DPO, and RFT training methodologies.
Provides data validation scripts for SFT, DPO, and RFT formats.
Facilitates training job submission and monitoring on Microsoft Foundry.
Enables calibration of RFT graders and pass thresholds.
Offers tools for model evaluation and deployment via ARM REST API.
Includes utilities for synthetic data generation and dataset quality scoring.

When to use it

When you need to adapt a base model to specific tasks or domains.
When preparing or validating training datasets for SFT, DPO, or RFT.
When training models for agentic scenarios involving tool calling.
When you need to evaluate and deploy a fine-tuned model.
When troubleshooting training jobs or diagnosing poor model performance.

Advantages

Standardized workflows for different training types.
Includes built-in validation to prevent common data formatting errors.
Supports complex agentic RFT training with tool integration.
Provides robust scripts for monitoring and evaluating training progress.
Offers clear guidance on grader design and hyperparameter tuning.

Usage examples

“How do I validate my SFT dataset before training?”: The agent will instruct you to use the python scripts/validate/validate_sft.py script on your data.jsonl file to ensure it meets the required format.
“Submit an SFT training job using gpt-4.1-mini.”: The agent will provide the command using scripts/submit_training.py with the appropriate flags for model, training file, validation file, and type.
“How can I calibrate my RFT grader?”: The agent will guide you to use scripts/calibrate_grader.py to find the optimal pass_threshold, aiming for a 25-50% failure rate on the base model.
“Deploy my fine-tuned model after training.”: The agent will provide the command to use scripts/deploy_model.py, specifying the model-id and a deployment name.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/microsoft/finetuning/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip finetuning.zip -d .agents/skills

Documentation by Microsoft

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

Files

ATTRIBUTION.mdAdded by AutoGineer998 B
LICENSELicence file added by AutoGineer1.1 KiB
SKILL.md5.4 KiB
references/agentic-rft.md3.2 KiB
references/dataset-formats.md3.7 KiB
references/deployment.md3.4 KiB
references/evaluation.md5.8 KiB
references/grader-design.md3.4 KiB
references/hyperparameters.md3.2 KiB
references/large-file-uploads.md927 B
references/platform-gotchas.md2.0 KiB
references/reward-hacking.md2.9 KiB
references/training-curves.md4.1 KiB
references/training-types.md3.1 KiB
references/vision-fine-tuning.md4.1 KiB
scripts/calibrate_grader.pyScript9.3 KiB
scripts/check_training.pyScript7.8 KiB
scripts/cleanup.pyScript9.5 KiB
scripts/common.pyScript7.6 KiB
scripts/convert_dataset.pyScript10.7 KiB
scripts/deploy_model.pyScript9.4 KiB
scripts/evaluate_model.pyScript11.4 KiB
scripts/generate_distillation_data.pyScript10.1 KiB
scripts/monitor_training.pyScript5.7 KiB
scripts/score_dataset.pyScript7.7 KiB
scripts/submit_training.pyScript10.8 KiB
scripts/validate/__init__.pyScript305 B
scripts/validate/data_stats.pyScript6.2 KiB
scripts/validate/validate_dpo.pyScript3.7 KiB
scripts/validate/validate_rft.pyScript8.6 KiB
scripts/validate/validate_sft.pyScript4.5 KiB
workflows/dataset-creation.md3.1 KiB
workflows/diagnose-poor-results.md2.3 KiB
workflows/full-pipeline.md3.1 KiB
workflows/iterative-training.md3.1 KiB
workflows/quickstart.md4.5 KiB

references/platform-gotchas.md

Platform Gotchas — Top 10

OSS models require "trainingType": "globalStandard" in the request body — undocumented, and all OSS FT jobs fail without it.
Model catalog fine_tune flag is wrong for OSS models — API returns fine_tune = false for all OSS models despite being FT-supported. Hardcode the supported list.
Older SDK versions may fail on /v1/ project endpoints — client.files.create() throws "API version not supported" with older openai package versions. Upgrade to openai>=1.0 and use the /v1/ project endpoint (preferred). If you must use an older SDK, fall back to REST API with the non-project /openai/ endpoint.
ARM "Succeeded" doesn't mean deployment is ready — provisioningState: Succeeded but data plane returns DeploymentNotReady indefinitely. Delete and recreate the deployment, then wait ~5 minutes.
OSS FT deployments may fail with InternalServerError — use the correct provider-specific model.format (e.g., "Mistral AI" not "OpenAI") and try capacity=100.
OSS FT inference hits "Failed to load LoRA" intermittently — deploy with capacity ≥ 100, use 8+ retries with exponential backoff, and wait 2+ minutes after deployment before first call.
ARM REST and az cognitiveservices use different format strings for OSS models — ARM uses provider names ("Microsoft", "Meta"), CLI uses "OpenAI-OSS" for all OSS. Mixing them produces HTTP 500.
Content safety false positives on entity extraction data — PII-dense data (medical records, legal docs, resumes) can trigger "Hate/Fairness" blocks at deployment time. Remove problematic document types.
FT deployments at capacity=1 are severely rate-limited (~1 RPM) — evaluating 10 samples takes ~10 minutes. Use capacity ≥ 100 for eval workloads and exponential backoff.
Wrong resource endpoint is a silent killer — jobs submitted to the wrong Foundry resource succeed via API but don't appear in the portal. Always verify the endpoint matches your Foundry project.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

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Attribution and licence

Created by

Microsoft

Publisher

Microsoft (Official)

Source

microsoft/skills · .github/plugins/azure-skills/skills/microsoft-foundry/finetuning · commit 0696600

Licence

MIT License (redistribution allowed)

LICENSE (added by AutoGineer from .github/plugins/azure-skills/LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

Also distributed in

microsoft/azure-skills

AutoGineer is not affiliated with or endorsed by Microsoft or Microsoft.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download finetuning.zip