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).

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リポジトリ: microsoft/skills
ソースの最終更新活動: 2026年8月7日 11:17
検出された SKILL.md の言語: 英語
スター: 2,885
フォーク: 328

インストール方法

デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。

Codex または Claude でインストールこの Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。

直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。

npx skills add https://github.com/microsoft/skills --skill finetuning

コマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。

ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。

ソースファイルを確認

インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。

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34 ファイル

SKILL.md

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microsoft-foundry

Deploy, evaluate, fine-tune, and manage Foundry agents end-to-end with azd: hosted agent scaffold/run/deploy, prompt agent create, batch eval, continuous eval, prompt optimizer, Agent Optimizer scaffold, agent.yaml, dataset curation from traces, model fine-tuning (SFT/DPO/RFT). USE FOR: azd ai agent, azd provision/deploy, deploy agent, hosted agent, create agent, add tool to agent, invoke agent, evaluate agent, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, optimize agent instructions, agent optimizer, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, AI Services, create Foundry resource, provision, knowledge index, customize deployment, onboard, availability, fine-tune, SFT, DPO, RFT, training-data, grader, distillation, fine-tuned model, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

2026-08-11

azure-kusto-graph

Build and query Kusto graphs from natural language. Covers transient graphs (make-graph), persistent graph models/snapshots, pattern matching (graph-match), shortest paths, connected components, and graph-to-table export. Generates the edges-first thinking: define edges, define node lookups, union, make-graph. WHEN: make-graph, graph-match, graph-shortest-paths, graph-to-table, graph-mark-components, persistent graph, graph model, graph snapshot, build a graph from data, find paths between nodes, pattern matching in graph, connected components, transient graph, Kusto graph, KQL graph.

2026-08-10

azure-kusto-irql-graph

Apply IRQL graph functions to KQL or IRQL query results for Kusto Explorer visualization. Generates Lift_To_Graph mappings and composes Graph_Render_View, Graph_Fold_By_Property, Extract_Node_*, Enrich_Node_*, and Enrich_Graph_* calls. Accepts a supplied query or limited basic natural-language source request; it is not a general natural-language-to-KQL/IRQL skill. WHEN: Lift_To_Graph, Graph_Render_View, Graph_Fold_By_Property, IRQL graph enrichment, graph mapping for existing query results, icon-decorated graph, fold graph nodes. Use azure-kusto-graph for native make-graph analysis, graph-match, shortest paths, components, or persistent graphs.

2026-08-10

このリポジトリのすべてを見る

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
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

finetuning

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このリポジトリの他の Skills

このリポジトリの他の Skills

Fine-Tuning on Microsoft Foundry

When to Use

Workflows

References

Scripts

Rules

Quick Reference

Error Handling

Fine-Tuning on Microsoft Foundry

When to Use

Workflows

References

Scripts

Rules

Quick Reference

Error Handling