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Can you use your own data to fine-tune Azure OpenAI models?
Learn if you can fine-tune Azure OpenAI models using your own custom data. Get a detailed explanation for the AI-900 exam on which models support fine-tuning, the process involved, and the benefits of creating a customized AI model in the Azure environment.
Question
You can fine-tune some Azure OpenAI models by using your own data.
A. True
B. False
Answer
A. True
Explanation
The statement is true. The Azure OpenAI Service allows you to perform fine-tuning on specific base models by using your own training data, creating a new, customized model tailored to your specific needs.
Understanding Fine-Tuning in Azure OpenAI
Fine-tuning is a process of further training a pre-trained foundation model on a specific, smaller dataset that you provide. This adapts the model’s behavior to better understand certain nuances, follow specific instructions, or produce output in a particular format or style. The result is a new custom model that is derived from the base model but optimized for your particular use case, often leading to higher quality results than can be achieved through prompt engineering alone.
Supported Models for Fine-Tuning
Not all models in the Azure OpenAI Service support fine-tuning, but the capability is available for several powerful models. The ability to fine-tune is dependent on the model series and version.
- GPT-3.5-Turbo: Fine-tuning is supported for versions of the gpt-35-turbo model. This is a common choice for creating customized models for chat or instruction-following tasks.
- Babbage and Davinci: The older generation models, specifically babbage-002 and davinci-002, also support fine-tuning.
- GPT-4: Fine-tuning for GPT-4 models is available as a specialized program, indicating that while possible, it may have different access requirements compared to other models.
The Fine-Tuning Process
The process generally involves three main steps within the Azure AI ecosystem:
- Prepare and Upload Data: You must create a dataset formatted with high-quality examples of prompt-and-completion pairs. This training data is then uploaded to your Azure resources.
- Train a New Model: You select a compatible base model and initiate a fine-tuning job using your prepared dataset. This can be done through the Azure AI Studio interface or programmatically using the API.
- Deploy the Custom Model: Once the fine-tuning job is complete, a new custom model is created. You can then deploy this model to an endpoint, making it available for inference requests just like a standard base model.
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