@@ -47,7 +47,7 @@ You also want to avoid mixing different schema designs. Don't build half of your
 
 ## Use standard training before advanced training
 
-[Standard training](../how-to/train-model.md#training-modes) is free and faster than advanced training. It can help you quickly understand the effect of changing your training set or schema while you build the model. After you're satisfied with the schema, consider using advanced training to get the best AIQ out of your model.
+[Standard training](../how-to/train-model.md#training-modes) is free and faster than advanced training. It can help you quickly understand the effect of changing your training set or schema while you build the model. After you're satisfied with the schema, consider using advanced training to get the best model quality.
 
 ## Use the evaluation feature
 
@@ -113,7 +113,7 @@ If you enable this feature, the utterance count of your training set increases.
 
 ## Address model overconfidence
 
-Customers can use the LoraNorm recipe version if the model is being incorrectly overconfident. An example of this behavior can be like the following scenario where the model predicts the incorrect intent with 100% confidence. This score makes the confidence threshold project setting unusable.
+Customers can use the LoraNorm traning configuration version if the model is being incorrectly overconfident. An example of this behavior can be like the following scenario where the model predicts the incorrect intent with 100% confidence. This score makes the confidence threshold project setting unusable.
 
 | Text |	Predicted intent |	Confidence score |
 |----|----|----|
@@ -243,7 +243,7 @@ curl --request POST \
 
 ## Address out-of-domain utterances
 
-Customers can use the newly updated recipe version `2024-08-01-preview` (previously `2024-06-01-preview`) if the model has poor AIQ on out-of-domain utterances. An example of this scenario with the default recipe can be like the following example where the model has three intents: `Sports`, `QueryWeather`, and `Alarm`. The test utterances are out-of-domain utterances and the model classifies them as `InDomain` with a relatively high confidence score.
+Customers can use the newly updated training configuration version `2024-08-01-preview` (previously `2024-06-01-preview`) if the model has poor quality on out-of-domain utterances. An example of this scenario with the default training configuration can be like the following example where the model has three intents: `Sports`, `QueryWeather`, and `Alarm`. The test utterances are out-of-domain utterances and the model classifies them as `InDomain` with a relatively high confidence score.
 
 | Text |	Predicted intent |	Confidence score |
 |----|----|----|
@@ -273,6 +273,6 @@ After the request is sent, you can track the progress of the training job in Lan
 
 Caveats:
 
-- The None score threshold for the app (confidence threshold below which `topIntent` is marked as `None`) when you use this recipe should be set to 0. This setting is used because this new recipe attributes a certain portion of the in-domain probabilities to out of domain so that the model isn't incorrectly overconfident about in-domain utterances. As a result, users might see slightly reduced confidence scores for in-domain utterances as compared to the prod recipe.
-- We don't recommend this recipe for apps with only two intents, such as `IntentA` and `None`, for example.
-- We don't recommend this recipe for apps with a low number of utterances per intent. We highly recommend a minimum of 25 utterances per intent.
+- The None score threshold for the app (confidence threshold below which `topIntent` is marked as `None`) when you use this training configuration should be set to 0. This setting is used because this new training configuration attributes a certain portion of the in-domain probabilities to out of domain so that the model isn't incorrectly overconfident about in-domain utterances. As a result, users might see slightly reduced confidence scores for in-domain utterances as compared to the prod training configuration.
+- We don't recommend this training configuration for apps with only two intents, such as `IntentA` and `None`, for example.
+- We don't recommend this training configuration for apps with a low number of utterances per intent. We highly recommend a minimum of 25 utterances per intent.

@@ -26,7 +26,6 @@ You get several hub default resources in your resource group. You need to config
 - Disable public network access of hub default resources such as Azure Storage, Azure Key Vault, and Azure Container Registry.
 - Establish private endpoint connection to hub default resources. You need to have both a blob and file private endpoint for the default storage account.
 - [Managed identity configurations](#managed-identity-configuration) to allow hubs access your storage account if it's private.
-- Azure AI Search should be public.
 
 
 ## Prerequisites
@@ -280,7 +279,6 @@ To check AI-PROJECT-GUID, go to the Azure portal, select your project, settings,
 
 ## Limitations
 
-* Private Azure AI Services and Azure AI Search aren't supported.
 * The "Add your data" feature in the Azure AI Studio playground doesn't support private storage account.
 * You might encounter problems trying to access the private endpoint for your hub if you're using Mozilla Firefox. This problem might be related to DNS over HTTPS in Mozilla Firefox. We recommend using Microsoft Edge or Google Chrome.
 

@@ -12,13 +12,14 @@ ms.date: 5/21/2024
 ms.reviewer: deeikele
 ms.author: larryfr
 author: Blackmist
+# Customer Intent: As an admin, I need to create and manage an Azure AI Studio hub so that my team can use it to create projects for collaboration.
 ---
 
 # How to create and manage an Azure AI Studio hub
 
 In AI Studio, hubs provide the environment for a team to collaborate and organize work, and help you as a team lead or IT admin centrally set up security settings and govern usage and spend. You can create and manage a hub from the Azure portal or from the AI Studio. 
 
-In this article, you learn how to create and manage a hub in AI Studio with the default settings so you can get started quickly. Do you need to customize security or the dependent resources of your hub? Then use [Azure Portal](create-secure-ai-hub.md) or [template options](create-azure-ai-hub-template.md). 
+In this article, you learn how to create and manage a hub in AI Studio with the default settings so you can get started quickly. Do you need to customize security or the dependent resources of your hub? Then use [Azure portal](create-secure-ai-hub.md) or [template options](create-azure-ai-hub-template.md). 
 
 > [!TIP]
 > If you'd like to create your Azure AI Studio hub using a template, see the articles on using [Bicep](create-azure-ai-hub-template.md) or [Terraform](create-hub-terraform.md).
@@ -105,7 +106,7 @@ For hubs that use CMK encryption mode, you can update the encryption key to a ne
 
 To use custom environments for Prompt Flow, you're required to configure an Azure Container Registry for your hub. To use Azure Application Insights for Prompt Flow deployments, a configured Azure Application Insights resource is required for your hub. Updating the workspace-attached Azure Container Registry or ApplicationInsights resources may break lineage of previous jobs, deployed inference endpoints, or your ability to rerun earlier jobs in the workspace. 
 
-You can use the Azure Portal, Azure SDK/CLI options, or the infrastructure-as-code templates to update both Azure Application Insights and Azure Container Registry for the hub.
+You can use the Azure portal, Azure SDK/CLI options, or the infrastructure-as-code templates to update both Azure Application Insights and Azure Container Registry for the hub.
 
 # [Azure portal](#tab/portal)
 
@@ -142,7 +143,23 @@ az ml workspace update -n "myexamplehub" -g "{MY_RESOURCE_GROUP}" -a "APPLICATIO
 ```
 ---
 
-## Next steps
+## Delete an Azure AI Studio hub
+
+To delete a hub, use the [Azure portal](https://portal.azure.com). To quickly get to the Azure portal from the Azure AI Studio, go to the **Hub overview** for your hub and then select **Manage in Azure portal**.
+
+:::image type="content" source="../media/how-to/hubs/manage-hub-azure-portal.png" alt-text="Screenshot of the manage in Azure portal link in Azure AI Studio.":::
+
+From the portal page for your hub, select **Overview** along the left side of the page and then select **Delete** from the top of the page.
+
+:::image type="content" source="../media/how-to/hubs/delete-hub-button.png" alt-text="Screenshot of the delete button for the Azure AI Studio hub in the Azure portal.":::
+
+You can also find your hub in the Azure portal by entering the hub name in the search field at the top of the Azure portal. Select the hub from the **Resources** list to navigate to the **Overview** page for the hub.
+
+:::image type="content" source="../media/how-to/hubs/search-in-portal.png" alt-text="Screenshot of using the search field in the Azure portal to find a hub.":::
+
+
+
+## Related content
 
 - [Create a project](create-projects.md)
 - [Learn more about Azure AI Studio](../what-is-ai-studio.md)

@@ -214,12 +214,12 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format=ChatCompletionsResponseFormatText(),
+    response_format={ "type": ChatCompletionsResponseFormatText() },
 )
 ```
 
 > [!WARNING]
-> Jais doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Jais models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -482,7 +482,7 @@ var response = await client.path("/chat/completions").post({
 ```
 
 > [!WARNING]
-> Jais doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Jais models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -580,7 +580,7 @@ Deployment to a serverless API endpoint doesn't require quota from your subscrip
 
 ### The inference package installed
 
-You can consume predictions from this model by using the `Azure.AI.Inference` package from [Nuget](https://www.nuget.org/). To install this package, you need the following prerequisites:
+You can consume predictions from this model by using the `Azure.AI.Inference` package from [NuGet](https://www.nuget.org/). To install this package, you need the following prerequisites:
 
 * The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2).
 * Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string.
@@ -606,7 +606,7 @@ using Azure.Identity;
 using Azure.AI.Inference;
 ```
 
-This example also use the following namespaces but you may not always need them:
+This example also uses the following namespaces but you may not always need them:
 
 
 ```csharp
@@ -775,7 +775,7 @@ Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}");
 ```
 
 > [!WARNING]
-> Jais doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Jais models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -1088,7 +1088,7 @@ Explore other parameters that you can specify in the inference client. For a ful
 ```
 
 > [!WARNING]
-> Jais doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Jais models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -1165,14 +1165,14 @@ The following example shows how to handle events when the model detects harmful
 
 ## More inference examples
 
-For more examples of how to use Jais, see the following examples and tutorials:
+For more examples of how to use Jais models, see the following examples and tutorials:
 
 | Description                               | Language          | Sample                                                          |
 |-------------------------------------------|-------------------|-----------------------------------------------------------------|
 | Azure AI Inference package for JavaScript | JavaScript        | [Link](https://aka.ms/azsdk/azure-ai-inference/javascript/samples)  |
 | Azure AI Inference package for Python     | Python            | [Link](https://aka.ms/azsdk/azure-ai-inference/python/samples)  |
 
-## Cost and quota considerations for Jais family of models deployed as serverless API endpoints
+## Cost and quota considerations for Jais models deployed as serverless API endpoints
 
 Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios.
 
@@ -1189,4 +1189,4 @@ For more information on how to track costs, see [Monitor costs for models offere
 * [Deploy models as serverless APIs](deploy-models-serverless.md)
 * [Consume serverless API endpoints from a different Azure AI Studio project or hub](deploy-models-serverless-connect.md)
 * [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md)
-* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace)
\ No newline at end of file
+* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace)

@@ -268,12 +268,12 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format=ChatCompletionsResponseFormatText(),
+    response_format={ "type": ChatCompletionsResponseFormatText() },
 )
 ```
 
 > [!WARNING]
-> Meta Llama doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Meta Llama models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -610,7 +610,7 @@ var response = await client.path("/chat/completions").post({
 ```
 
 > [!WARNING]
-> Meta Llama doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Meta Llama models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -765,7 +765,7 @@ For deployment to a self-hosted managed compute, you must have enough quota in y
 
 ### The inference package installed
 
-You can consume predictions from this model by using the `Azure.AI.Inference` package from [Nuget](https://www.nuget.org/). To install this package, you need the following prerequisites:
+You can consume predictions from this model by using the `Azure.AI.Inference` package from [NuGet](https://www.nuget.org/). To install this package, you need the following prerequisites:
 
 * The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2).
 * Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string.
@@ -791,7 +791,7 @@ using Azure.Identity;
 using Azure.AI.Inference;
 ```
 
-This example also use the following namespaces but you may not always need them:
+This example also uses the following namespaces but you may not always need them:
 
 
 ```csharp
@@ -973,7 +973,7 @@ Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}");
 ```
 
 > [!WARNING]
-> Meta Llama doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Meta Llama models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -1348,7 +1348,7 @@ Explore other parameters that you can specify in the inference client. For a ful
 ```
 
 > [!WARNING]
-> Meta Llama doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Meta Llama models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -1441,7 +1441,7 @@ The following example shows how to handle events when the model detects harmful
 
 ## More inference examples
 
-For more examples of how to use Meta Llama, see the following examples and tutorials:
+For more examples of how to use Meta Llama models, see the following examples and tutorials:
 
 | Description                               | Language          | Sample                                                             |
 |-------------------------------------------|-------------------|------------------------------------------------------------------- |
@@ -1453,7 +1453,7 @@ For more examples of how to use Meta Llama, see the following examples and tutor
 | LangChain                                 | Python            | [Link](https://aka.ms/meta-llama-3.1-405B-instruct-langchain)      |
 | LiteLLM                                   | Python            | [Link](https://aka.ms/meta-llama-3.1-405B-instruct-litellm)        | 
 
-## Cost and quota considerations for Meta Llama family of models deployed as serverless API endpoints
+## Cost and quota considerations for Meta Llama models deployed as serverless API endpoints
 
 Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios.
 
@@ -1463,7 +1463,7 @@ Each time a project subscribes to a given offer from the Azure Marketplace, a ne
 
 For more information on how to track costs, see [Monitor costs for models offered through the Azure Marketplace](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace).
 
-## Cost and quota considerations for Meta Llama family of models deployed to managed compute
+## Cost and quota considerations for Meta Llama models deployed to managed compute
 
 Meta Llama models deployed to managed compute are billed based on core hours of the associated compute instance. The cost of the compute instance is determined by the size of the instance, the number of instances running, and the run duration.
 

@@ -5,7 +5,7 @@ description: Learn how to use Mistral Nemo chat model with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly
 ms.topic: how-to
-ms.date: 08/08/2024
+ms.date: 09/13/2024
 ms.reviewer: kritifaujdar
 reviewer: fkriti
 ms.author: mopeakande
@@ -222,7 +222,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format=ChatCompletionsResponseFormatText(),
+    response_format={ "type": ChatCompletionsResponseFormatText() },
 )
 ```
 
@@ -242,7 +242,7 @@ response = client.complete(
                       " the following format: { ""answer"": ""response"" }."),
         UserMessage(content="How many languages are in the world?"),
     ],
-    response_format=ChatCompletionsResponseFormatJSON()
+    response_format={ "type": ChatCompletionsResponseFormatJSON() }
 )
 ```
 
@@ -962,7 +962,7 @@ Deployment to a serverless API endpoint doesn't require quota from your subscrip
 
 ### The inference package installed
 
-You can consume predictions from this model by using the `Azure.AI.Inference` package from [Nuget](https://www.nuget.org/). To install this package, you need the following prerequisites:
+You can consume predictions from this model by using the `Azure.AI.Inference` package from [NuGet](https://www.nuget.org/). To install this package, you need the following prerequisites:
 
 * The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2).
 * Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string.
@@ -988,7 +988,7 @@ using Azure.Identity;
 using Azure.AI.Inference;
 ```
 
-This example also use the following namespaces but you may not always need them:
+This example also uses the following namespaces but you may not always need them:
 
 
 ```csharp
@@ -2010,7 +2010,7 @@ The following example shows how to handle events when the model detects harmful
 
 ## More inference examples
 
-For more examples of how to use Mistral, see the following examples and tutorials:
+For more examples of how to use Mistral models, see the following examples and tutorials:
 
 | Description                               | Language          | Sample                                                          |
 |-------------------------------------------|-------------------|-----------------------------------------------------------------|
@@ -2024,7 +2024,7 @@ For more examples of how to use Mistral, see the following examples and tutorial
 | LiteLLM                                   | Python            | [Link](https://aka.ms/mistral-large/litellm-sample)             | 
 
 
-## Cost and quota considerations for Mistral family of models deployed as serverless API endpoints
+## Cost and quota considerations for Mistral models deployed as serverless API endpoints
 
 Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios.
 
@@ -2041,4 +2041,4 @@ For more information on how to track costs, see [Monitor costs for models offere
 * [Deploy models as serverless APIs](deploy-models-serverless.md)
 * [Consume serverless API endpoints from a different Azure AI Studio project or hub](deploy-models-serverless-connect.md)
 * [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md)
-* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace)
\ No newline at end of file
+* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace)

@@ -5,7 +5,7 @@ description: Learn how to use Mistral-7B and Mixtral chat models with Azure AI S
 ms.service: azure-ai-studio
 manager: scottpolly
 ms.topic: how-to
-ms.date: 08/08/2024
+ms.date: 09/13/2024
 ms.reviewer: kritifaujdar
 reviewer: fkriti
 ms.author: mopeakande
@@ -63,8 +63,8 @@ Mixtral 8x22B comes with the following strengths:
 
 * Fluent in English, French, Italian, German, and Spanish
 * Strong mathematics and coding capabilities
-* Natively capable of function calling; along with the constrained output mode implemented on la Plateforme, this enables application development and tech stack modernization at scale
-* Pprecise information recall from large documents, due to its 64K tokens context window
+* Natively capable of function calling, enabling application development and tech stack modernization at scale
+* Precise information recall from large documents, due to its 64K tokens context window
 
 
 The following models are available:
@@ -270,12 +270,12 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format=ChatCompletionsResponseFormatText(),
+    response_format={ "type": ChatCompletionsResponseFormatText() },
 )
 ```
 
 > [!WARNING]
-> Mistral doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Mistral models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -352,8 +352,8 @@ Mixtral 8x22B comes with the following strengths:
 
 * Fluent in English, French, Italian, German, and Spanish
 * Strong mathematics and coding capabilities
-* Natively capable of function calling; along with the constrained output mode implemented on la Plateforme, this enables application development and tech stack modernization at scale
-* Pprecise information recall from large documents, due to its 64K tokens context window
+* Natively capable of function calling, enabling application development and tech stack modernization at scale
+* Precise information recall from large documents, due to its 64K tokens context window
 
 
 The following models are available:
@@ -576,7 +576,7 @@ var response = await client.path("/chat/completions").post({
 ```
 
 > [!WARNING]
-> Mistral doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Mistral models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -658,8 +658,8 @@ Mixtral 8x22B comes with the following strengths:
 
 * Fluent in English, French, Italian, German, and Spanish
 * Strong mathematics and coding capabilities
-* Natively capable of function calling; along with the constrained output mode implemented on la Plateforme, this enables application development and tech stack modernization at scale
-* Pprecise information recall from large documents, due to its 64K tokens context window
+* Natively capable of function calling, enabling application development and tech stack modernization at scale
+* Precise information recall from large documents, due to its 64K tokens context window
 
 
 The following models are available:
@@ -689,7 +689,7 @@ For deployment to a self-hosted managed compute, you must have enough quota in y
 
 ### The inference package installed
 
-You can consume predictions from this model by using the `Azure.AI.Inference` package from [Nuget](https://www.nuget.org/). To install this package, you need the following prerequisites:
+You can consume predictions from this model by using the `Azure.AI.Inference` package from [NuGet](https://www.nuget.org/). To install this package, you need the following prerequisites:
 
 * The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2).
 * Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string.
@@ -715,7 +715,7 @@ using Azure.Identity;
 using Azure.AI.Inference;
 ```
 
-This example also use the following namespaces but you may not always need them:
+This example also uses the following namespaces but you may not always need them:
 
 
 ```csharp
@@ -897,7 +897,7 @@ Console.WriteLine($"Response: {response.Value.Choices[0].Message.Content}");
 ```
 
 > [!WARNING]
-> Mistral doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Mistral models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -976,8 +976,8 @@ Mixtral 8x22B comes with the following strengths:
 
 * Fluent in English, French, Italian, German, and Spanish
 * Strong mathematics and coding capabilities
-* Natively capable of function calling; along with the constrained output mode implemented on la Plateforme, this enables application development and tech stack modernization at scale
-* Pprecise information recall from large documents, due to its 64K tokens context window
+* Natively capable of function calling, enabling application development and tech stack modernization at scale
+* Precise information recall from large documents, due to its 64K tokens context window
 
 
 The following models are available:
@@ -1230,7 +1230,7 @@ Explore other parameters that you can specify in the inference client. For a ful
 ```
 
 > [!WARNING]
-> Mistral doesn't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
+> Mistral models don't support JSON output formatting (`response_format = { "type": "json_object" }`). You can always prompt the model to generate JSON outputs. However, such outputs are not guaranteed to be valid JSON.
 
 If you want to pass a parameter that isn't in the list of supported parameters, you can pass it to the underlying model using *extra parameters*. See [Pass extra parameters to the model](#pass-extra-parameters-to-the-model).
 
@@ -1279,7 +1279,7 @@ The following extra parameters can be passed to Mistral-7B and Mixtral chat mode
 
 ## More inference examples
 
-For more examples of how to use Mistral, see the following examples and tutorials:
+For more examples of how to use Mistral models, see the following examples and tutorials:
 
 | Description                               | Language          | Sample                                                          |
 |-------------------------------------------|-------------------|-----------------------------------------------------------------|
@@ -1293,7 +1293,7 @@ For more examples of how to use Mistral, see the following examples and tutorial
 | LiteLLM                                   | Python            | [Link](https://aka.ms/mistral-large/litellm-sample)             | 
 
 
-## Cost and quota considerations for Mistral family of models deployed to managed compute
+## Cost and quota considerations for Mistral models deployed to managed compute
 
 Mistral models deployed to managed compute are billed based on core hours of the associated compute instance. The cost of the compute instance is determined by the size of the instance, the number of instances running, and the run duration.
 
@@ -1306,4 +1306,4 @@ It is a good practice to start with a low number of instances and scale up as ne
 * [Deploy models as serverless APIs](deploy-models-serverless.md)
 * [Consume serverless API endpoints from a different Azure AI Studio project or hub](deploy-models-serverless-connect.md)
 * [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md)
-* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace)
\ No newline at end of file
+* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace)

@@ -5,7 +5,7 @@ description: Learn how to use Mistral premium chat models with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly
 ms.topic: how-to
-ms.date: 08/08/2024
+ms.date: 09/13/2024
 ms.reviewer: kritifaujdar
 reviewer: fkriti
 ms.author: mopeakande
@@ -252,7 +252,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format=ChatCompletionsResponseFormatText(),
+    response_format={ "type": ChatCompletionsResponseFormatText() },
 )
 ```
 
@@ -272,7 +272,7 @@ response = client.complete(
                       " the following format: { ""answer"": ""response"" }."),
         UserMessage(content="How many languages are in the world?"),
     ],
-    response_format=ChatCompletionsResponseFormatJSON()
+    response_format={ "type": ChatCompletionsResponseFormatJSON() }
 )
 ```
 
@@ -1052,7 +1052,7 @@ Deployment to a serverless API endpoint doesn't require quota from your subscrip
 
 ### The inference package installed
 
-You can consume predictions from this model by using the `Azure.AI.Inference` package from [Nuget](https://www.nuget.org/). To install this package, you need the following prerequisites:
+You can consume predictions from this model by using the `Azure.AI.Inference` package from [NuGet](https://www.nuget.org/). To install this package, you need the following prerequisites:
 
 * The endpoint URL. To construct the client library, you need to pass in the endpoint URL. The endpoint URL has the form `https://your-host-name.your-azure-region.inference.ai.azure.com`, where `your-host-name` is your unique model deployment host name and `your-azure-region` is the Azure region where the model is deployed (for example, eastus2).
 * Depending on your model deployment and authentication preference, you need either a key to authenticate against the service, or Microsoft Entra ID credentials. The key is a 32-character string.
@@ -1078,7 +1078,7 @@ using Azure.Identity;
 using Azure.AI.Inference;
 ```
 
-This example also use the following namespaces but you may not always need them:
+This example also uses the following namespaces but you may not always need them:
 
 
 ```csharp
@@ -2130,7 +2130,7 @@ The following example shows how to handle events when the model detects harmful
 
 ## More inference examples
 
-For more examples of how to use Mistral, see the following examples and tutorials:
+For more examples of how to use Mistral models, see the following examples and tutorials:
 
 | Description                               | Language          | Sample                                                          |
 |-------------------------------------------|-------------------|-----------------------------------------------------------------|
@@ -2144,7 +2144,7 @@ For more examples of how to use Mistral, see the following examples and tutorial
 | LiteLLM                                   | Python            | [Link](https://aka.ms/mistral-large/litellm-sample)             | 
 
 
-## Cost and quota considerations for Mistral family of models deployed as serverless API endpoints
+## Cost and quota considerations for Mistral models deployed as serverless API endpoints
 
 Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios.
 
@@ -2161,4 +2161,4 @@ For more information on how to track costs, see [Monitor costs for models offere
 * [Deploy models as serverless APIs](deploy-models-serverless.md)
 * [Consume serverless API endpoints from a different Azure AI Studio project or hub](deploy-models-serverless-connect.md)
 * [Region availability for models in serverless API endpoints](deploy-models-serverless-availability.md)
-* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace)
\ No newline at end of file
+* [Plan and manage costs (marketplace)](costs-plan-manage.md#monitor-costs-for-models-offered-through-the-azure-marketplace)

@@ -5,7 +5,7 @@ description: Learn how to use Phi-3.5 MoE chat model with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly
 ms.topic: how-to
-ms.date: 08/19/2024
+ms.date: 09/13/2024
 ms.reviewer: kritifaujdar
 reviewer: fkriti
 ms.author: mopeakande
@@ -232,7 +232,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format=ChatCompletionsResponseFormatText(),
+    response_format={ "type": ChatCompletionsResponseFormatText() },
 )
 ```
 

@@ -5,7 +5,7 @@ description: Learn how to use Phi-3 chat model with vision with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly
 ms.topic: how-to
-ms.date: 08/19/2024
+ms.date: 09/13/2024
 ms.reviewer: kritifaujdar
 reviewer: fkriti
 ms.author: mopeakande
@@ -228,7 +228,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format=ChatCompletionsResponseFormatText(),
+    response_format={ "type": ChatCompletionsResponseFormatText() },
 )
 ```
 

@@ -5,7 +5,7 @@ description: Learn how to use Phi-3 family chat models with Azure AI Studio.
 ms.service: azure-ai-studio
 manager: scottpolly
 ms.topic: how-to
-ms.date: 08/19/2024
+ms.date: 09/13/2024
 ms.reviewer: kritifaujdar
 reviewer: fkriti
 ms.author: mopeakande
@@ -269,7 +269,7 @@ response = client.complete(
     stop=["<|endoftext|>"],
     temperature=0,
     top_p=1,
-    response_format=ChatCompletionsResponseFormatText(),
+    response_format={ "type": ChatCompletionsResponseFormatText() },
 )
 ```
 

@@ -230,7 +230,7 @@ To fine-tune a Llama 2 model in an existing Azure AI Studio project, follow thes
 
     :::image type="content" source="../media/how-to/fine-tune/llama/llama-pay-as-you-go-overview.png" alt-text="Screenshot of pay-as-you-go marketplace overview." lightbox="../media/how-to/fine-tune/llama/llama-pay-as-you-go-overview.png":::
 
-1. Choose a base model to fine-tune and select **Confirm**. Your choice influences both the performance and [the cost of your model](./deploy-models-llama.md#cost-and-quota-considerations-for-meta-llama-family-of-models-deployed-as-serverless-api-endpoints).
+1. Choose a base model to fine-tune and select **Confirm**. Your choice influences both the performance and [the cost of your model](./deploy-models-llama.md#cost-and-quota-considerations-for-meta-llama-models-deployed-as-serverless-api-endpoints).
 
     :::image type="content" source="../media/how-to/fine-tune/llama/fine-tune-select-model.png" alt-text="Screenshot of option to select a model to fine-tune." lightbox="../media/how-to/fine-tune/llama/fine-tune-select-model.png":::
 

@@ -51,14 +51,14 @@ items:
       href: concepts/ai-resources.md
     - name: Create your first hub
       href: how-to/create-azure-ai-resource.md
+    - name: Create a hub using the Azure Machine Learning SDK and CLI
+      href: how-to/develop/create-hub-project-sdk.md
     - name: Create a hub with custom security
       items:
       - name: Create a hub in the Azure portal
         href: how-to/create-secure-ai-hub.md
       - name: Create a hub from template
         href: how-to/create-azure-ai-hub-template.md
-      - name: Create a hub using the Azure Machine Learning SDK and CLI
-        href: how-to/develop/create-hub-project-sdk.md
         displayName: code
       - name: Create a hub using Terraform
         href: how-to/create-hub-terraform.md