# finetuning

A skill that walks through model fine-tuning on Microsoft Foundry: dataset prep, job submission, deployment, evaluation. Useful when the base model's behaviour does not fit.

- skill · `type=skill` · Claude Code, GitHub Copilot · ★ 3149
- Author: Microsoft
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-skills/skills/microsoft-foundry/finetuning/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/finetuning

## What it does

The skill covers the whole training path: SFT, DPO and RFT with graders. It ships Python scripts. `submit_training.py` sends a job, `monitor_training.py` polls it to completion, `check_training.py` reads training curves and lists checkpoints, `deploy_model.py` deploys through the ARM REST API, and `evaluate_model.py` scores the result with an LLM judge. A converter moves datasets between SFT, DPO and RFT formats, a generator produces synthetic data for distillation, and validators live in `scripts/validate/`.

Before submitting anything, the skill wants a baseline measurement on the base model. For RFT it advises tuning the grader so the base model fails 25-50% of examples, and it warns against deploying the last checkpoint without looking at the others. Token cost should be tracked next to accuracy.

Several failures are spelled out. An older `openai` SDK against the `/v1/` endpoint returns “API version not supported”, so `openai>=1.0` is needed. Open models reject Standard TrainingType and want `globalStandard`; the script takes a `--use-rest` flag or falls back on its own. A job can hang in post-training eval when the tool endpoint is under-provisioned, and a successful ARM call can still leave you with “DeploymentNotReady” — delete the deployment and recreate it. Training data dense with PII gets blocked by content safety at deployment time.

## Requirements
- Works with: Azure

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add microsoft/skills --skill finetuning -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/finetuning/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/finetuning/

### Codex

**Manually: put the files in place**
- File: `.agents/skills/finetuning/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

**Manually: put the files in place**
- File: `.cursor/skills/finetuning/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install microsoft/skills finetuning
```

2. **Manually: put the files in place**
- File: `.github/skills/finetuning/SKILL.md`

### Gemini CLI

**Platform CLI**
```bash
gemini skills install https://github.com/microsoft/skills --path .github/plugins/azure-skills/skills/microsoft-foundry/finetuning
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/finetuning/SKILL.md` — The legacy .windsurf/skills/ path still works

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-skills/skills/microsoft-foundry/finetuning/SKILL.md

## When to use it
- prepare and validate a dataset before submitting a job
- pick between SFT, DPO and RFT for a given task
- calibrate the pass threshold for an RFT grader
- read training curves and choose a checkpoint over the final one
- generate synthetic data for distillation

## Example prompts
- Prepare an SFT dataset and run `scripts/validate/validate_sft.py` on it
- Submit an SFT job on `gpt-4.1-mini` and monitor it to completion
- Calibrate the grader for RFT and find the pass_threshold

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 6f1b67847e230f62.

BEGIN THIRD-PARTY FILE 6f1b67847e230f62
```text
---
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) |
```
END THIRD-PARTY FILE 6f1b67847e230f62

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