finetuning
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, 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).
Profile →npx skills add microsoft/azure-skills --skill finetuning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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](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
- When to Use
- Workflows
- References
- Scripts
- Rules
- Quick Reference
- Error Handling
What does the finetuning skill do?
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, 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).
How do I install it?
Run `npx skills add microsoft/azure-skills --skill finetuning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From microsoft/azure-skills, a repository with 1,353 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.