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articles/ai-services/openai/includes/fine-tuning-openai-in-ai-studio.md

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:::image type="content" source="../media/fine-tuning/checkpoints.png" alt-text="Screenshot of checkpoints UI." lightbox="../media/fine-tuning/checkpoints.png":::
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## Safety evaluation for GPT-4 fine-tuning (preview)
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## Safety evaluation GPT-4 fine-tuning - public preview
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[!INCLUDE [Safety evaluation](../includes/safety-evaluation.md)]
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articles/ai-services/openai/includes/fine-tuning-python.md

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## Safety evaluation for GPT-4 fine-tuning (preview)
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## Safety evaluation GPT-4 fine-tuning - public preview
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[!INCLUDE [Safety evaluation](../includes/safety-evaluation.md)]
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articles/ai-services/openai/includes/fine-tuning-rest.md

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-H "api-key: $AZURE_OPENAI_API_KEY"
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```
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## Safety evaluation for GPT-4 fine-tuning (preview)
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## Safety evaluation GPT-4 fine-tuning - public preview
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[!INCLUDE [Safety evaluation](../includes/safety-evaluation.md)]
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articles/ai-services/openai/includes/fine-tuning-studio.md

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When each training epoch completes a checkpoint is generated. A checkpoint is a fully functional version of a model which can both be deployed and used as the target model for subsequent fine-tuning jobs. Checkpoints can be particularly useful, as they can provide a snapshot of your model prior to overfitting having occurred. When a fine-tuning job completes you will have the three most recent versions of the model available to deploy.
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## Safety evaluation for GPT-4 fine-tuning (preview)
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## Safety evaluation GPT-4 fine-tuning - public preview
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[!INCLUDE [Safety evaluation](../includes/safety-evaluation.md)]
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