Merge pull request #160 from mrbullwinkle/mrb_09_05_2024_fine_tuning_002

[Azure OpenAI] Fine-tuning updates

Author: prmerger-automator[bot]

articles/ai-services/openai/concepts/models.md

@@ -313,8 +313,9 @@ These models can only be used with Embedding API requests.
 | `gpt-35-turbo` (0125)  | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | 16,385 | Sep 2021 |
 | `gpt-4` (0613) <sup>**1**</sup> | North Central US <br> Sweden Central | 8192 | Sep 2021 |
 | `gpt-4o-mini` <sup>**1**</sup> (2024-07-18) | North Central US <br> Sweden Central | Input: 128,000 <br> Output: 16,384  <br> Training example context length: 64,536 | Oct 2023 |
+| `gpt-4o` <sup>**1**</sup> (2024-08-06) | North Central US <br> Sweden Central | Input: 128,000 <br> Output: 16,384  <br> Training example context length: 64,536 | Oct 2023 | 
 
-**<sup>1</sup>** GPT-4 and GPT-4o mini fine-tuning is currently in public preview. See our [GPT-4 & GPT-4o mini fine-tuning safety evaluation guidance](/azure/ai-services/openai/how-to/fine-tuning?tabs=turbo%2Cpython-new&pivots=programming-language-python#safety-evaluation-gpt-4-fine-tuning---public-preview) for more information.
+**<sup>1</sup>** GPT-4, GPT-4o, and GPT-4o mini fine-tuning is currently in public preview. See our [GPT-4, GPT-4o,  & GPT-4o mini fine-tuning safety evaluation guidance](/azure/ai-services/openai/how-to/fine-tuning?tabs=turbo%2Cpython-new&pivots=programming-language-python#safety-evaluation-gpt-4-fine-tuning---public-preview) for more information.
 
 ### Whisper models
 

articles/ai-services/openai/how-to/fine-tuning-functions.md

@@ -5,7 +5,7 @@ description: Learn how to improve function calling performance with Azure OpenAI
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 02/05/2024
+ms.date: 09/05/2024
 author: mrbullwinkle
 ms.author: mbullwin
 ---
@@ -18,9 +18,6 @@ Models that use the chat completions API support [function calling](../how-to/fu
 * Get similarly formatted responses even when the full function definition isn't present. (Allowing you to potentially save money on prompt tokens.)
 * Get more accurate and consistent outputs.
 
-> [!IMPORTANT]
-> The `functions` and `function_call` parameters have been deprecated with the release of the [`2023-12-01-preview`](https://github.com/Azure/azure-rest-api-specs/blob/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2023-12-01-preview/inference.json) version of the API. However, the fine-tuning API currently requires use of the legacy parameters.
-
 ## Constructing a training file
 
 When constructing a training file of function calling examples, you would take a function definition like this:

articles/ai-services/openai/includes/safety-evaluation.md

@@ -1,7 +1,7 @@
 ---
-title: 'Safety evaluation - GPT-4 fine tuning only public preview'
+title: 'Safety evaluation - GPT-4o, GPT-4o-mini, & GPT-4 fine tuning only public preview'
 titleSuffix: Azure OpenAI
-description: Learn about how to perform fine-tuning with GPT-4 models.
+description: Learn about how to perform fine-tuning with GPT-4o, GPT-4o-mini, and GPT-4 models.
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: include
@@ -10,13 +10,13 @@ author: mrbullwinkle
 ms.author: mbullwin
 ---
 
-GPT-4 and GPT-4o-mini are our most advanced models that can be fine-tuned to your needs. As with Azure OpenAI models generally, the advanced capabilities of fine-tuned models come with increased responsible AI challenges related to harmful content, manipulation, human-like behavior, privacy issues, and more. Learn more about risks, capabilities, and limitations in the [Overview of Responsible AI practices](/legal/cognitive-services/openai/overview?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext) and [Transparency Note](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fcognitive-services%2Fopenai%2Fcontext%2Fcontext&tabs=text). To help mitigate the risks associated with GPT-4 and GPT-4o-mini fine-tuned models, we have implemented additional evaluation steps to help detect and prevent harmful content in the training and outputs of fine-tuned models. These steps are grounded in the [Microsoft Responsible AI Standard](https://www.microsoft.com/ai/responsible-ai) and [Azure OpenAI Service content filtering](/azure/ai-services/openai/concepts/content-filter?tabs=warning%2Cpython-new).
+GPT-4o, GPT-4o-mini, and GPT-4 are our most advanced models that can be fine-tuned to your needs. As with Azure OpenAI models generally, the advanced capabilities of fine-tuned models come with increased responsible AI challenges related to harmful content, manipulation, human-like behavior, privacy issues, and more. Learn more about risks, capabilities, and limitations in the [Overview of Responsible AI practices](/legal/cognitive-services/openai/overview?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext) and [Transparency Note](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fcognitive-services%2Fopenai%2Fcontext%2Fcontext&tabs=text). To help mitigate the risks associated with advanced fine-tuned models, we have implemented additional evaluation steps to help detect and prevent harmful content in the training and outputs of fine-tuned models. These steps are grounded in the [Microsoft Responsible AI Standard](https://www.microsoft.com/ai/responsible-ai) and [Azure OpenAI Service content filtering](/azure/ai-services/openai/concepts/content-filter?tabs=warning%2Cpython-new).
 
 - Evaluations are conducted in dedicated, customer specific, private workspaces;
 - Evaluation endpoints are in the same geography as the Azure OpenAI resource;
 - Training data is not stored in connection with performing evaluations; only the final model assessment (deployable or not deployable) is persisted; and
 
-GPT-4 and GPT-4o-mini 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 may have created.
+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 may have created.
 
 ### Data evaluation