modify according to feedback

Author: likebupt

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

@@ -32,7 +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.
+The UI does not support cross region deployment, while Python SDK or REST supports.
 
 ## [Python](#tab/python)
 
@@ -42,13 +42,13 @@ import json
 import os
 import requests
 
-token= os.getenv("<TOKEN>") 
+token = os.getenv("<TOKEN>") 
 subscription = "<YOUR_SUBSCRIPTION_ID>"  
 resource_group = "<YOUR_RESOURCE_GROUP_NAME>"
 resource_name = "<YOUR_AZURE_OPENAI_RESOURCE_NAME>"
-model_deployment_name ="gpt-35-turbo-ft" # custom deployment name that you will use to reference the model when making inference calls.
+model_deployment_name = "gpt-35-turbo-ft" # custom deployment name that you will use to reference the model when making inference calls.
 
-deploy_params = {'api-version': "2023-05-01"} 
+deploy_params = {'api-version': "2024-10-21"} 
 deploy_headers = {'Authorization': 'Bearer {}'.format(token), 'Content-Type': 'application/json'}
 
 deploy_data = {
@@ -110,9 +110,9 @@ source_resource = "<SOURCE_RESOURCE>"
 
 source = f'/subscriptions/{source_subscription}/resourceGroups/{source_resource_group}/providers/Microsoft.CognitiveServices/accounts/{source_resource}'
 
-model_deployment_name ="gpt-35-turbo-ft" # custom deployment name that you will use to reference the model when making inference calls.
+model_deployment_name = "gpt-35-turbo-ft" # custom deployment name that you will use to reference the model when making inference calls.
 
-deploy_params = {'api-version': "2023-05-01"} 
+deploy_params = {'api-version': "2024-10-21"} 
 deploy_headers = {'Authorization': 'Bearer {}'.format(token), 'Content-Type': 'application/json'}
 
 
@@ -198,7 +198,7 @@ The following example shows how to use the REST API to create a model deployment
 
 
 ```bash
-curl -X POST "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.CognitiveServices/accounts/<RESOURCE_NAME>/deployments/<MODEL_DEPLOYMENT_NAME>api-version=2023-05-01" \
+curl -X POST "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.CognitiveServices/accounts/<RESOURCE_NAME>/deployments/<MODEL_DEPLOYMENT_NAME>api-version=2024-10-21" \
   -H "Authorization: Bearer <TOKEN>" \
   -H "Content-Type: application/json" \
   -d '{
@@ -232,7 +232,7 @@ The only limitations are that the new region must also support fine-tuning and w
 Below is an example of deploying a model that was fine-tuned in one subscription/region to another.
 
 ```bash
-curl -X PUT "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.CognitiveServices/accounts/<RESOURCE_NAME>/deployments/<MODEL_DEPLOYMENT_NAME>api-version=2023-05-01" \
+curl -X PUT "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.CognitiveServices/accounts/<RESOURCE_NAME>/deployments/<MODEL_DEPLOYMENT_NAME>api-version=2024-10-21" \
   -H "Authorization: Bearer <TOKEN>" \
   -H "Content-Type: application/json" \
   -d '{
@@ -339,7 +339,7 @@ print(response.choices[0].message.content)
 ## [REST](#tab/rest)
 
 ```bash
-curl $AZURE_OPENAI_ENDPOINT/openai/deployments/<deployment_name>/chat/completions?api-version=2023-05-15 \
+curl $AZURE_OPENAI_ENDPOINT/openai/deployments/<deployment_name>/chat/completions?api-version=2024-10-21 \
   -H "Content-Type: application/json" \
   -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?"}]}'
@@ -457,7 +457,7 @@ To delete a deployment, use the [Deployments - Delete REST API](/rest/api/aiserv
 Below is the REST API example to delete a deployment:
 
 ```bash
-curl -X DELETE "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.CognitiveServices/accounts/<RESOURCE_NAME>/deployments/<MODEL_DEPLOYMENT_NAME>api-version=2024-10-01" \
+curl -X DELETE "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.CognitiveServices/accounts/<RESOURCE_NAME>/deployments/<MODEL_DEPLOYMENT_NAME>api-version=2024-10-21" \
   -H "Authorization: Bearer <TOKEN>"
 ```
 

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

@@ -64,7 +64,6 @@ If you set the detail parameter for an image to low, the image is resized
 
 ```json
 { 
-
     "type": "image_url", 
 
     "image_url": { 
@@ -74,7 +73,6 @@ If you set the detail parameter for an image to low, the image is resized
         "detail": "low" 
 
     } 
-
 } 
 ```
 

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

@@ -64,7 +64,7 @@ Your training data and validation data sets consist of input and output examples
 
 The training and validation data you use **must** be formatted as a JSON Lines (JSONL) document. For `gpt-35-turbo-0613` the fine-tuning dataset must be formatted in the conversational format that is used by the [Chat completions](../how-to/chatgpt.md) API.
 
-It's generally recommened to use the instructions and prompts that you found worked best in every training example. This will help you get the best results, especially if you have fewer than a hundred examples.
+It's generally recommended to use the instructions and prompts that you found worked best in every training example. This will help you get the best results, especially if you have fewer than a hundred examples.
 
 ### Example file format
 
@@ -184,7 +184,7 @@ Your job might be queued behind other jobs on the system. Training your model ca
 
 ## Checkpoints
 
-When each training epoch completes a checkpoint is generated. A checkpoint is a fully functional version of a model which can both be deployed and used as the target model for subsequent fine-tuning jobs. Checkpoints can be particularly useful, as they can provide a snapshot of your model prior to overfitting having occurred. When a fine-tuning job completes you will have the three most recent versions of the model available to deploy.
+When each training epoch completes a checkpoint is generated. A checkpoint is a fully functional version of a model which can both be deployed and used as the target model for subsequent fine-tuning jobs. Checkpoints can be particularly useful, as they may provide snapshots prior to overfitting. When a fine-tuning job completes you will have the three most recent versions of the model available to deploy.
 
 :::image type="content" source="../media/fine-tuning/checkpoints.png" alt-text="Screenshot of checkpoints UI." lightbox="../media/fine-tuning/checkpoints.png":::
 

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

@@ -125,7 +125,7 @@ from openai import AzureOpenAI
 client = AzureOpenAI(
   azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
   api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
-  api_version="2024-05-01-preview"  # This API version or later is required to access seed/events/checkpoint capabilities
+  api_version="2024-10-21"  # This API version or later is required to access seed/events/checkpoint capabilities
 )
 
 training_file_name = 'training_set.jsonl'
@@ -264,7 +264,7 @@ print(response.model_dump_json(indent=2))
 
 ## Checkpoints
 
-When each training epoch completes a checkpoint is generated. A checkpoint is a fully functional version of a model which can both be deployed and used as the target model for subsequent fine-tuning jobs. Checkpoints can be particularly useful, as they can provide a snapshot of your model prior to overfitting having occurred. When a fine-tuning job completes you will have the three most recent versions of the model available to deploy. The final epoch will be represented by your fine-tuned model, the previous two epochs will be available as checkpoints.
+When each training epoch completes a checkpoint is generated. A checkpoint is a fully functional version of a model which can both be deployed and used as the target model for subsequent fine-tuning jobs. Checkpoints can be particularly useful, as they may provide snapshots prior to overfitting. When a fine-tuning job completes you will have the three most recent versions of the model available to deploy. The final epoch will be represented by your fine-tuned model, the previous two epochs will be available as checkpoints.
 
 You can run the list checkpoints command to retrieve the list of checkpoints associated with an individual fine-tuning job. You might need to upgrade your OpenAI client library to the latest version with `pip install openai --upgrade` to run this command.
 
@@ -340,7 +340,7 @@ resource_group = "<YOUR_RESOURCE_GROUP_NAME>"
 resource_name = "<YOUR_AZURE_OPENAI_RESOURCE_NAME>"
 model_deployment_name ="gpt-35-turbo-ft" # custom deployment name that you will use to reference the model when making inference calls.
 
-deploy_params = {'api-version': "2023-05-01"} 
+deploy_params = {'api-version': "2024-10-21"} 
 deploy_headers = {'Authorization': 'Bearer {}'.format(token), 'Content-Type': 'application/json'}
 
 deploy_data = {

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

@@ -137,7 +137,7 @@ After you uploaded your training and validation files, you're ready to start the
 In this example we are also passing the seed parameter. The seed controls the reproducibility of the job. Passing in the same seed and job parameters should produce the same results, but can differ in rare cases. If a seed is not specified, one will be generated for you.
 
 ```bash
-curl -X POST $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs?api-version=2024-05-01-preview \
+curl -X POST $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs?api-version=2024-10-21 \
   -H "Content-Type: application/json" \
   -H "api-key: $AZURE_OPENAI_API_KEY" \
   -d '{
@@ -164,7 +164,7 @@ The current supported hyperparameters for fine-tuning are:
 After you start a fine-tune job, it can take some time to complete. Your job might be queued behind other jobs in the system. Training your model can take minutes or hours depending on the model and dataset size. The following example uses the REST API to check the status of your fine-tuning job. The example retrieves information about your job by using the job ID returned from the previous example:
 
 ```bash
-curl -X GET $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs/<YOUR-JOB-ID>?api-version=2024-05-01-preview \
+curl -X GET $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs/<YOUR-JOB-ID>?api-version=2024-10-21 \
   -H "api-key: $AZURE_OPENAI_API_KEY"
 ```
 
@@ -173,19 +173,19 @@ curl -X GET $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs/<YOUR-JOB-ID>?api-ver
 To examine the individual fine-tuning events that were generated during training:
 
 ```bash
-curl -X POST $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs/{fine_tuning_job_id}/events?api-version=2024-05-01-preview \
+curl -X POST $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs/{fine_tuning_job_id}/events?api-version=2024-10-21 \
   -H "Content-Type: application/json" \
   -H "api-key: $AZURE_OPENAI_API_KEY" 
 ```
 
 ## Checkpoints
 
-When each training epoch completes a checkpoint is generated. A checkpoint is a fully functional version of a model which can both be deployed and used as the target model for subsequent fine-tuning jobs. Checkpoints can be particularly useful, as they can provide a snapshot of your model prior to overfitting having occurred. When a fine-tuning job completes you will have the three most recent versions of the model available to deploy. The final epoch will be represented by your fine-tuned model, the previous two epochs will be available as checkpoints.
+When each training epoch completes a checkpoint is generated. A checkpoint is a fully functional version of a model which can both be deployed and used as the target model for subsequent fine-tuning jobs. Checkpoints can be particularly useful, as they may provide snapshots prior to overfitting. When a fine-tuning job completes you will have the three most recent versions of the model available to deploy. The final epoch will be represented by your fine-tuned model, the previous two epochs will be available as checkpoints.
 
 You can run the list checkpoints command to retrieve the list of checkpoints associated with an individual fine-tuning job:
 
 ```bash
-curl -X POST $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs/{fine_tuning_job_id}/checkpoints?api-version=2024-05-01-preview \
+curl -X POST $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs/{fine_tuning_job_id}/checkpoints?api-version=2024-10-21 \
   -H "Content-Type: application/json" \
   -H "api-key: $AZURE_OPENAI_API_KEY" 
 ```
@@ -240,7 +240,7 @@ The following example shows how to use the REST API to create a model deployment
 | fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-35-turbo-0613.ft-b044a9d3cf9c4228b5d393567f693b83`. You'll need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
 
 ```bash
-curl -X POST "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.CognitiveServices/accounts/<RESOURCE_NAME>/deployments/<MODEL_DEPLOYMENT_NAME>api-version=2023-05-01" \
+curl -X POST "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.CognitiveServices/accounts/<RESOURCE_NAME>/deployments/<MODEL_DEPLOYMENT_NAME>api-version=2024-10-21" \
   -H "Authorization: Bearer <TOKEN>" \
   -H "Content-Type: application/json" \
   -d '{

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

@@ -59,7 +59,7 @@ Your training data and validation data sets consist of input and output examples
 
 The training and validation data you use **must** be formatted as a JSON Lines (JSONL) document. For `gpt-35-turbo` (all versions), `gpt-4`, `gpt-4o`, and `gpt-4o-mini`, the fine-tuning dataset must be formatted in the conversational format that is used by the [Chat completions](../how-to/chatgpt.md) API.
 
-It's generally recommened to use the instructions and prompts that you found worked best in every training example. This will help you get the best results, especially if you have fewer than a hundred examples.
+It's generally recommended to use the instructions and prompts that you found worked best in every training example. This will help you get the best results, especially if you have fewer than a hundred examples.
 
 
 ### Example file format
@@ -97,7 +97,7 @@ In addition to the JSONL format, training and validation data files must be enco
 
 ### Datasets size consideration
 
-The more training examples you have, the better. Fine tuning jobs will not proceed without at least 10 training examples, but such a small number isn't enough to noticeably influence model responses. It is best practice to provide hundreds, if not thousands, of training examples to be successful. It's recommened to start with 50 weel-crafted training data.
+The more training examples you have, the better. Fine-tuning jobs will not proceed without at least 10 training examples, but such a small number isn't enough to noticeably influence model responses. It is best practice to provide hundreds, if not thousands, of training examples to be successful. It's recommended to start with 50 well-crafted training data.
 
 In general, doubling the dataset size can lead to a linear increase in model quality. But keep in mind, low quality examples can negatively impact performance. If you train the model on a large amount of internal data, without first pruning the dataset for only the highest quality examples you could end up with a model that performs much worse than expected.
 
@@ -184,13 +184,13 @@ Review your choices and select **Submit** to start training your new fine-tuned
 
 ## Check the status of your custom model
 
-After you submit your fine-tuning job, you see a page with details about your fine-tuned model. You can find the status and more information about your fine-tuned model on the **Fine-tuning** page in Azure AI Foundry portal.
+After you submit your fine-tuning job, you will see a page with details about your fine-tuned model. You can find the status and more information about your fine-tuned model on the **Fine-tuning** page in Azure AI Foundry portal.
 
 Your job might be queued behind other jobs on the system. Training your model can take minutes or hours depending on the model and dataset size.
 
 ## Checkpoints
 
-When each training epoch completes a checkpoint is generated. A checkpoint is a fully functional version of a model which can both be deployed and used as the target model for subsequent fine-tuning jobs. Checkpoints can be particularly useful, as they can provide a snapshot of your model prior to overfitting having occurred. When a fine-tuning job completes you will have the three most recent versions of the model available to deploy.
+When each training epoch completes a checkpoint is generated. A checkpoint is a fully functional version of a model which can both be deployed and used as the target model for subsequent fine-tuning jobs. Checkpoints can be particularly useful, as they may provide snapshots prior to overfitting. When a fine-tuning job completes you will have the three most recent versions of the model available to deploy.
 
 ## Analyze your custom model