Fine Tuning Models

Naveen Krishnan

Azure OpenAI Service allows you to customize models to your specific datasets through a process called fine-tuning. This customization step enhances the model’s performance by training it on more examples than can fit into a prompt, leading to higher quality results, token savings, and lower-latency requests. Fine-tuning adjusts the base model’s weights to improve performance on specific tasks, reducing the need for extensive prompt engineering.

Prerequisites

To get started with fine-tuning, you need:

• An Azure subscription.
• An Azure OpenAI resource.
• Specific Python libraries: os, json, requests, openai.
• Fine-tuning access with Cognitive Services OpenAI Contributor permissions.

Models Supporting Fine-Tuning

The following models support fine-tuning:

• babbage-002
• davinci-002
• gpt-35-turbo (various versions)
• gpt-4 (public preview)
• gpt-4o-mini (public preview)

Fine-Tuning Workflow

The fine-tuning workflow involves several steps:

Read More: Visit Medium.com