finetuning

1# Fine-Tuning on Microsoft Foundry
2 
3Fine-tune models using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset prep, training, deployment, and evaluation.
4 
5## When to Use
6 
7Use this sub-skill when the user asks about:
8- Fine-tuning a model (SFT, DPO, or RFT)
9- Preparing, validating, or formatting training data
10- Submitting, monitoring, or diagnosing training jobs
11- Calibrating graders or pass thresholds for RFT
12- Deploying or evaluating a fine-tuned model
13- Choosing between training types (SFT vs DPO vs RFT)
14- Distillation, synthetic data generation, or dataset quality scoring
15- Large file uploads for training data
16- Cleaning up fine-tuning resources (files, deployments)
17 
18**Do NOT use for:** General model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).
19 
20## Workflows
21 
22| Stage | Guide |
23|-------|-------|
24| **Quick start** | [workflows/quickstart.md](workflows/quickstart.md) |
25| **Full pipeline** | [workflows/full-pipeline.md](workflows/full-pipeline.md) |
26| **Create data** | [workflows/dataset-creation.md](workflows/dataset-creation.md) |
27| **Iterate** | [workflows/iterative-training.md](workflows/iterative-training.md) |
28| **Diagnose** | [workflows/diagnose-poor-results.md](workflows/diagnose-poor-results.md) |
29 
30## References
31 
32| Topic | File |
33|-------|------|
34| SFT vs DPO vs RFT | [references/training-types.md](references/training-types.md) |
35…