removing info

Author: aahill

articles/ai-services/agents/concepts/model-region-support.md

@@ -7,7 +7,7 @@ author: aahill
 ms.author: aahi
 ms.service: azure-ai-agent-service
 ms.topic: conceptual
-ms.date: 01/07/2025
+ms.date: 01/27/2025
 ms.custom: azure-ai-agents
 ---
 
@@ -28,48 +28,19 @@ Azure AI Agent Service supports the same models as the chat completions API in A
 | westus        | ✅                         | ✅                          | ✅                            | -                   | ✅                          | -                           | ✅                               | - | -                          | ✅                         | ✅                          | -                              |
 
 
-## More models
+## Non-microsoft models
 
 The Azure AI Agent Service also supports the following models from the Azure AI Foundry model catalog.
 
 * Llama 3.1-70B-instruct
 * Mistral-large-2407
 * Cohere command R+
 
-To use these models, you can use Azure AI Foundry portal to make a deployment, and then reference it in your agent. 
-
-1. Go to the [Azure AI Foundry portal](https://ai.azure.com/) and select **Model catalog** in the left navigation menu, and scroll down to **Meta-Llama-3-70B-Instruct**. You can also find and use one of the models listed previously.  
-
-1. Select **Deploy**. 
-
-1. In the Deployment options screen that appears, select **Serverless API** with Azure AI Content Safety. 
-
-    :::image type="content" source="../media/llama/llama-deployment.png" alt-text="An image of the llama model project selection screen.":::
- 
-1. Select your project and then select **Subscribe and deploy**. 
-
-    :::image type="content" source="../media/llama/llama-deployment-2.png" alt-text="An image of the llama model deployment screen.":::
-
-1. Add the serverless connection to your hub/project. The deployment name you choose is the one that you reference in your code.  
-
-1. When calling agent creation API, set the `models` parameter to your deployment name. For example:
-
-    # [Python](#tab/python)
-
-    ```python
-    agent = project_client.agents.create_agent( model="llama-3", name="my-agent", instructions="You are a helpful agent" ) 
-    ```
-
-    # [C#](#tab/csharp)
-
-    ```csharp
-    Response<Agent> agentResponse = await client.CreateAgentAsync(
-            model: "llama-3",
-            name: "My agent",
-            instructions: "You are a helpful agent"
-    ```
-    ---
+To use these models, you can use Azure AI Foundry portal to make a deployment, and then reference the deployment name in your agent. For example:
 
+```python
+agent = project_client.agents.create_agent( model="llama-3", name="my-agent", instructions="You are a helpful agent" ) 
+```
 
 ## Next steps
 

articles/ai-services/agents/how-to/tools/openapi-spec.md

@@ -187,7 +187,7 @@ An example of the audience would be ```https://cognitiveservices.azure.com/```.
 # Create agent with OpenAPI tool and process assistant run
 with project_client:
     agent = project_client.agents.create_agent(
-        model="gpt-4o-mini",
+        model="gpt-4o",
         name="my-assistant",
         instructions="You are a helpful assistant",
         tools=openapi.definitions
@@ -201,7 +201,7 @@ with project_client:
 # [C#](#tab/csharp)
 ```csharp
 Response<Agent> agentResponse = await client.CreateAgentAsync(
-            model: "gpt-4",
+            model: "gpt-4o",
             name: "azure-function-agent-foo",
             instructions: "You are a helpful assistant.",
             tools: new List<ToolDefinition> { openapiTool }