finetuning Skill
Fine-tune models on Azure AI 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, Published by microsoft in azure-skills.
What is finetuning Skill?
Fine-tune models on Azure AI 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, Published by microsoft in azure-skills. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Deployment
- Data analysis
- Deployment use cases
- Data analysis use cases
Technical details
- Install or run with Copy skill directory
When to use finetuning Skill
- Use it for deployment.
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: microsoft
- Repository: microsoft/azure-skills
- Skill file: .github/plugins/azure-skills/skills/microsoft-foundry/finetuning/SKILL.md
What it does
Fine-tune models on Azure AI 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,
Skill instructions
Fine-Tuning on Azure AI 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-pi
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Frequently asked questions
What is finetuning?
finetuning is a open-source AI agent skill with Copy skill directory. Fine-tune models on Azure AI Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation.
Who is finetuning best for?
finetuning is best for reusing agent instructions, scripts, and references, deployment workflows, data analysis workflows.
How do I install finetuning?
Install or run finetuning using Copy skill directory. Check finetuning for the latest setup command.
Is finetuning actively maintained?
finetuning may need a closer maintenance check before production use.
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