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articles/ai-foundry/openai/how-to/fine-tuning-safety-evaluation.md

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@@ -17,9 +17,9 @@ GPT-4o, GPT-4o-mini, and GPT-4 are our most advanced models that can be fine-tun
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- Evaluations are conducted in dedicated, customer specific, private workspaces;
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- Evaluation endpoints are in the same geography as the Azure OpenAI resource;
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- Training data is not stored in connection with performing evaluations; only the final model assessment (deployable or not deployable) is persisted; and
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- Training data isn't stored in connection with performing evaluations; only the final model assessment (deployable or not deployable) is persisted; and
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GPT-4o, GPT-4o-mini, and GPT-4 fine-tuned model evaluation filters are set to predefined thresholds and cannot be modified by customers; they aren't tied to any custom content filtering configuration you might have created.
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GPT-4o, GPT-4o-mini, and GPT-4 fine-tuned model evaluation filters are set to predefined thresholds and can't be modified by customers; they aren't tied to any custom content filtering configuration you might have created.
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## Data evaluation
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As with data evaluation, the model is evaluated automatically within your fine-tuning job as part of providing the fine-tuning capability. Only the resulting assessment (deployable or not deployable) is logged by the service. If deployment of the fine-tuned model fails due to the detection of harmful content in model outputs, you won't be charged for the training run.
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## Next steps
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- To modify content safety for fine-tuning refer to terms listed in the [form](https://customervoice.microsoft.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR7en2Ais5pxKtso_Pz4b1_xUMlBQNkZMR0lFRldORTdVQzQ0TEI5Q1ExOSQlQCN0PWcu)
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- Explore the fine-tuning capabilities in the [Azure OpenAI fine-tuning tutorial](../tutorials/fine-tune.md).
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- Review fine-tuning [model regional availability](../concepts/models.md#fine-tuning-models)
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- Learn more about [Azure OpenAI quotas](../quotas-limits.md)

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