Azure-Samples/azureai-foundry-finetuning-raft
A recipe that will walk you through using either Meta Llama 3.1 405B or OpenAI GPT-4o deployed on Azure AI to generate a synthetic dataset using UC Berkeley's Gorilla project RAFT method.
Implements end-to-end RAFT fine-tuning via Azure Developer CLI (AZD) automation, handling synthetic dataset generation, model fine-tuning, deployment, and comparative evaluation in a single workflow. Supports flexible model composition—using large teacher models (GPT-4o or Llama 3.1 405B) to distill knowledge into smaller student models (GPT-4o-mini, Llama 3.1 8B)—with configurable embedding and judge models across both OpenAI and Azure Marketplace deployments. Provides infrastructure-as-code provisioning and environment variable management to support bring-your-own-models scenarios alongside managed Azure AI endpoints.
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Jul 17, 2025
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