A guide for fine-tuning models in Microsoft Foundry, which means training an existing model with task-specific examples or preferences. It covers supervised fine-tuning, preference training, and training with reward graders.

It is for preparing datasets, submitting and monitoring training jobs, setting up graders, deploying fine-tuned models, evaluating them, and handling large training files.

It helps turn raw training data and a chosen training method into a managed training, deployment, and evaluation process.

finetuning — Skill for Claude Code by microsoft: A guide for fine-tuning models in Microsoft Foundry, which…

About the project

Azure Skills Plugin is an agent plugin that packages Azure-specific guidance with MCP server configurations for carrying out Azure work. Coding agents use it to plan, deploy, troubleshoot, monitor, govern, and optimize Azure applications and services. The catalogue entries are the plugin's skills, instructions, and execution integrations.

microsoft/azure-skills · 1,453 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/microsoft/azure-skills/finetuning

Any agent

npx skills add microsoft/azure-skills --skill finetuning

Clone the repo

git clone --depth 1 https://github.com/microsoft/azure-skills

Made for: Claude Code.

Or install azure, the plugin that ships this one along with the rest of its 38 skills, 1 plugin.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/microsoft/azure-skills/finetuning.svg)](https://agentmods.dev/skills/microsoft/azure-skills/finetuning)

Your own site

<a href="https://agentmods.dev/skills/microsoft/azure-skills/finetuning"><img src="https://agentmods.dev/badge/skills/microsoft/azure-skills/finetuning.svg" alt="Measured on agentmods" height="20"></a>

Per session 132 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,378 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin 100% copy Near-identical to another mod in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00132	$0.01378
Opus 5	$0.00066	$0.00689
Sonnet 5	$0.00026	$0.00276
Haiku 4.5	$0.00013	$0.00138

Measured 6d ago against content hash 7a17789cb61e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

finetuning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

The scan reads SKILL.md. This mod also ships 16 executable files (scripts/calibrate_grader.py, scripts/check_training.py, scripts/cleanup.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to finetuning — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.github/plugins/azure-skills/skills/microsoft-foundry/finetuning/SKILL.md · 100 lines

How it starts

The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 100 lines

Files

What ships with it

33 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

6d ago First seen · 100 lines · 132 tokens per session scan A 7a17789cb61e

Subscribe to this mod's changes

finetuning is a skill published in the GitHub repository microsoft/azure-skills (1,453 stars, last pushed yesterday), licensed MIT. It adds 132 tokens to every session and 1,378 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to finetuning, differing in 0 lines, and is treated as a copy.

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