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.openpublishing.redirection.json

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articles/ai-studio/.openpublishing.redirection.ai-studio.json

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"source_path_from_root": "/articles/ai-studio/quickstarts/assistants.md",
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"redirect_url": "/azure/ai-services/openai/assistants-quickstart",
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articles/ai-studio/concepts/fine-tuning-overview.md

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Even with a great use case, fine-tuning is only as good as the quality of the data that you're able to provide. You need to be willing to invest the time and effort to make fine-tuning work. Different models will require different data volumes but you often need to be able to provide fairly large quantities of high-quality curated data.
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Another important point is even with high quality data if your data isn't in the necessary format for fine-tuning you'll need to commit engineering resources in order to properly format the data.
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| Data | Babbage-002 & Davinci-002 | GPT-3.5-Turbo |
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|---|---|---|
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| Volume | Thousands of Examples | Thousands of Examples |
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| Format | Prompt/Completion | Conversational Chat |
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Another important point is even with high quality data if your data isn't in the necessary format for fine-tuning you'll need to commit engineering resources in order to properly format the data. For more information on how to prepare your data for fine-tuning, refer to the [fine-tuning documentation](../../ai-services/openai/how-to/fine-tuning.md?context=/azure/ai-studio/context/context).
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**If you are ready for fine-tuning you:**
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## Related content
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- [Learn how to fine-tune an Azure OpenAI model in Azure AI Studio](../../ai-services/openai/how-to/fine-tuning.md?context=/azure/ai-studio/context/context)
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- [Learn how to fine-tune a Llama 2 model in Azure AI Studio](../how-to/fine-tune-model-llama.md)

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