Fine-tune Large Language Models on Azure AI Foundry using SFT, DPO, or RFT.
npx skills add https://github.com/mordechai30/.agents --skill finetuning-mordechai30
Please help me install this Agent Skill. Skill: finetuning Source: https://github.com/mordechai30/.agents/tree/main/skills/microsoft-foundry/finetuning Command: npx skills add https://github.com/mordechai30/.agents --skill finetuning-mordechai30
This skill streamlines the complex lifecycle of fine-tuning Large Language Models on Azure AI Foundry, removing the friction from dataset preparation, training, and deployment.
Use the finetuning skill to submit a supervised fine-tuning job using your training file train.jsonl and validation file val.jsonl.
High-intent search queries and answers about installing and using this skill.
To fine-tune an LLM on Azure AI Foundry, this skill automates the pipeline from dataset validation to deployment using SFT, DPO, or RFT methodologies. It manages training job submission and hyperparameter tuning.
SFT (supervised), DPO (preference), and RFT (reinforcement with graders) are distinct methodologies to optimize model behavior. This skill supports all three for refining reasoning or adapting models to proprietary documentation.
Yes, you can use JSONL files for supervised fine-tuning on Azure. The skill processes training and validation files, providing automated dataset conversion and validation before submitting the SFT job.
Yes, the skill manages model deployment after training. It handles the entire end-to-end pipeline, deploying the custom model on Azure infrastructure once the SFT, DPO, or RFT training job completes.
Required dependencies include openai, azure-identity, azure-ai-projects, requests, pandas, and pyarrow. These libraries enable dataset manipulation, Azure authentication, and training job submission.
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