fix format

Author: likebupt

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

@@ -32,6 +32,7 @@ The **Deploy model** dialog box opens. In the dialog box, enter your **Deploymen
 You can monitor the progress of your deployment on the **Deployments** pane in Azure AI Foundry portal.
 
 The UI does not support corss region deployment, while Python SDK or REST supports.
+---
 
 ## [Python](#tab/python)
 
@@ -190,6 +191,7 @@ response = requests.put(url, headers=headers, json=payload)
 print(f"Status Code: {response.status_code}")
 print(f"Response: {response.json()}")
 ```
+---
 
 ## [REST](#tab/rest)
 
@@ -297,7 +299,7 @@ az cognitiveservices account deployment create
     --sku-capacity "1" 
     --sku-name "Standard"
 ```
-
+---
 
 [!INCLUDE [Fine-tuning deletion](../includes/fine-tune.md)]
 
@@ -309,6 +311,8 @@ After your custom model deploys, you can use it like any other deployed model. Y
 
 :::image type="content" source="../media/quickstarts/playground-load-new.png" alt-text="Screenshot of the Playground pane in Azure AI Foundry portal, with sections highlighted." lightbox="../media/quickstarts/playground-load-new.png":::
 
+---
+
 ## [Python](#tab/python)
 
 ```python
@@ -333,7 +337,7 @@ response = client.chat.completions.create(
 
 print(response.choices[0].message.content)
 ```
-
+---
 
 ## [REST](#tab/rest)
 
@@ -343,6 +347,7 @@ curl $AZURE_OPENAI_ENDPOINT/openai/deployments/<deployment_name>/chat/completion
   -H "api-key: $AZURE_OPENAI_API_KEY" \
   -d '{"messages":[{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Does Azure OpenAI support customer managed keys?"},{"role": "assistant", "content": "Yes, customer managed keys are supported by Azure OpenAI."},{"role": "user", "content": "Do other Azure AI services support this too?"}]}'
 ```
+---
 
 ### Prompt caching
 

articles/ai-services/openai/includes/fine-tuning-studio.md

@@ -43,7 +43,7 @@ Take a moment to review the fine-tuning workflow for using Azure AI Foundry port
 
 1. Prepare your training and validation data.
 1. Use the **Create custom model** wizard in Azure AI Foundry portal to train your custom model.
-    1. [Select a base model](#select-the-base-model).
+    1. Select a base mode.
     1. [Choose your training data](#choose-your-training-data).
     1. Optionally, [choose your validation data](#choose-your-validation-data).
     1. Optionally, [configure task parameters](#configure-task-parameters) for your fine-tuning job.
@@ -125,9 +125,9 @@ The next step is to either choose existing prepared training data or upload new
 
 - To upload new training data, use one of the following options:
 
-   - Select **Local file** to [upload training data from a local file](#upload-training-data-from-local-file).
+   - Select **Local file** to upload training data from a local file.
 
-   - Select **Azure blob or other shared web locations** to [import training data from Azure Blob or another shared web location](#import-training-data-from-azure-blob-store).
+   - Select **Azure blob or other shared web locations** to import training data from Azure Blob or another shared web location.
 
 For large data files, we recommend that you import from an Azure Blob store. Large files can become unstable when uploaded through multipart forms because the requests are atomic and can't be retried or resumed. For more information about Azure Blob Storage, see [What is Azure Blob Storage](/azure/storage/blobs/storage-blobs-overview)?
 
@@ -148,9 +148,9 @@ The **Validation data** pane displays any existing, previously uploaded training
 
 - To upload new validation data, use one of the following options:
 
-   - Select **Local file** to [upload validation data from a local file](#upload-validation-data-from-local-file).
+   - Select **Local file** to upload validation data from a local file.
 
-   - Select **Azure blob or other shared web locations** to [import validation data from Azure Blob or another shared web location](#import-validation-data-from-azure-blob-store).
+   - Select **Azure blob or other shared web locations** to import validation data from Azure Blob or another shared web location.
 
 For large data files, we recommend that you import from an Azure Blob store. Large files can become unstable when uploaded through multipart forms because the requests are atomic and can't be retried or resumed.