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Finetuning

OrganizationPopular
microsoft
finetuning

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).

Overview

Publishermicrosoft
Repositoryazure-skills
Skill namefinetuning
Stars
1.5K
Forks
246
Bundled files
33
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 33 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by microsoft on GitHub. Read the source before you install it.

Installation

Install the Finetuning AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/microsoft/azure-skills.git /tmp/azure-skills
mkdir -p .claude/skills
cp -r /tmp/azure-skills/skills/microsoft-foundry/finetuning .claude/skills/finetuning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Finetuning in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Finetuning on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Finetuning is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

References

Scripts

ScriptPurpose
scripts/submit_training.pySubmit SFT/DPO/RFT jobs
scripts/monitor_training.pyPoll job until completion
scripts/calibrate_grader.pyFind optimal RFT pass_threshold
scripts/check_training.pyAnalyze curves, list checkpoints
scripts/deploy_model.pyDeploy via ARM REST API
scripts/evaluate_model.pyLLM judge evaluation
scripts/convert_dataset.pyConvert between SFT/DPO/RFT formats
scripts/generate_distillation_data.pyGenerate synthetic training data
scripts/score_dataset.pyQuality scoring on training data
scripts/cleanup.pyDelete 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

TaskCommand
Validate SFT datapython scripts/validate/validate_sft.py data.jsonl
Submit SFT jobpython scripts/submit_training.py --model gpt-4.1-mini --training-file train.jsonl --validation-file val.jsonl --type sft
Monitor jobpython scripts/monitor_training.py --job-id ftjob-xxx
Analyze curvespython scripts/check_training.py --job-id ftjob-xxx
Deploy modelpython scripts/deploy_model.py --model-id ft:gpt-4.1-mini:... --name my-eval
Evaluate modelpython scripts/evaluate_model.py --deployment-name my-eval --test-file test.jsonl

Error Handling

ErrorCauseFix
"API version not supported"Older openai SDK on /v1/ endpointUpgrade to openai>=1.0
"does not support fine-tuning with Standard TrainingType"OSS model needs globalStandardUse --use-rest flag or script auto-falls back
Job stuck in post-training evalUnder-provisioned tool endpoint (RFT)Scale to S2+, enable Always On
"DeploymentNotReady" after ARM succeedsARM/data-plane race conditionDelete and recreate deployment, wait 5 min
Content safety block at deploymentPII-dense training dataRemove problematic document types

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Finetuning AI skill do?

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).

Why use Finetuning on TypingMind?

Because you install it once and use it with any model. Finetuning is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Finetuning in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/azure-skills/tree/main/skills/microsoft-foundry/finetuning. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Finetuning?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Finetuning?

As many as you like. As long as a model supports skills, you can use Finetuning with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Finetuning AI skill free?

Yes. It is published on GitHub by microsoft under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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