finetuning Skill

Source

• Creator: microsoft
• Repository: microsoft/skills
• Skill file: .github/plugins/azure-skills/skills/microsoft-foundry/finetuning/SKILL.md

What it does

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,

Skill instructions

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-p