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@@ -850,7 +850,7 @@ Alternatively, you can deploy your fine-tuned model using any of the other commo
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| resource_group | The resource group name for your Azure OpenAI resource |
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| resource_name | The Azure OpenAI resource name |
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| model_deployment_name | The custom name for your new fine-tuned model deployment. This is the name that will be referenced in your code when making chat completion calls. |
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| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-4o-mini-2024-07-18.ft-b044a9d3cf9c4228b5d393567f693b83`. You'll need to add that value to the deploy_data json. |
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| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-4o-mini-2024-07-18.ft-0e208cf33a6a466994aff31a08aba678`. You'll need to add that value to the deploy_data json. |
"name": "<YOUR_FINE_TUNED_MODEL>", #retrieve this value from the previous call, it will look like gpt-4o-mini-2024-07-18.ft-b044a9d3cf9c4228b5d393567f693b83
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"name": "<YOUR_FINE_TUNED_MODEL>", #retrieve this value from the previous call, it will look like gpt-4o-mini-2024-07-18.ft-0e208cf33a6a466994aff31a08aba678
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"version": "1"
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}
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}
@@ -894,7 +894,7 @@ print(r.json())
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You can check on your deployment progress in the Azure OpenAI Studio:
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:::image type="content" source="../media/tutorials/fine-tuning/status.png" alt-text="Screenshot of the initial DataFrame table results from the CSV file." lightbox="../media/tutorials/fine-tuning/status.png":::
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:::image type="content" source="../media/tutorials/fine-tuning/studio-deployment-status.png" alt-text="Deployment progress on AOAI studio" lightbox="../media/tutorials/fine-tuning/studio-deployment-status.png":::
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It isn't uncommon for this process to take some time to complete when dealing with deploying fine-tuned models.
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