update

Author: mrbullwinkle

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

@@ -177,6 +177,17 @@ When each training epoch completes a checkpoint is generated. A checkpoint is a
 
 :::image type="content" source="../media/fine-tuning/checkpoints.png" alt-text="Screenshot of checkpoints UI." lightbox="../media/fine-tuning/checkpoints.png":::
 
+## Pause and resume
+
+You can track progress in both fine-tuning views of the AI Foundry portal. You'll see your job go through the same statuses as normal fine tuning jobs (queued, running, succeeded).
+
+You can also review the results files while training runs, to get a peak at the progress and whether your training is proceeding as expected.
+
+> [!NOTE]
+> During the training you can view the logs and metrics and pause the job as needed. Pausing can be useful, if metrics aren't converging or if you feel model isn't learning at the right pace. Once the training job is paused, a deployable checkpoint will be created once safety evals are complete. This checkpoint available for you to deploy and use for inference or resume the job further to completion. Pause operation is only applicable for jobs which have been trained for at least one step and are in *Running* state.
+
+:::image type="content" source="../media/how-to/reinforcement-fine-tuning/pause.png" alt-text="Screenshot of the reinforcement fine-tuning with a running job." lightbox="../media/how-to/reinforcement-fine-tuning/pause.png":::
+
 ## Analyze your fine-tuned model
 
 After fine-tuning is successfully completed, you can download a result file named _results.csv_ from the fine-tuned model page under the **Details** tab. You can use the result file to analyze the training and validation performance of your custom model. 

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

@@ -158,6 +158,26 @@ curl -X GET $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs/<YOUR-JOB-ID>?api-ver
   -H "api-key: $AZURE_OPENAI_API_KEY"
 ```
 
+## Pause and resume
+
+During the training you can view the logs and metrics and pause the job as needed. Pausing can be useful, if metrics aren't converging or if you feel model isn't learning at the right pace. Once the training job is paused, a deployable checkpoint will be created once safety evals are complete. This checkpoint available for you to deploy and use for inference or resume the job further to completion. Pause operation is only applicable for jobs which have been trained for at least one step and are in *Running* state.
+
+### Pause
+
+```bash
+curl -X POST $AZURE_OPENAI_ENDPOINT/openai/v1/fine_tuning/jobs/{fine_tuning_job_id}/pause \
+  -H "Content-Type: application/json" \
+  -H "api-key: $AZURE_OPENAI_API_KEY" 
+```
+
+### Resume
+
+```bash
+curl -X POST $AZURE_OPENAI_ENDPOINT/openai/v1/fine_tuning/jobs/{fine_tuning_job_id}/resume \
+  -H "Content-Type: application/json" \
+  -H "api-key: $AZURE_OPENAI_API_KEY" 
+```
+
 ### List fine-tuning events
 
 To examine the individual fine-tuning events that were generated during training:

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

@@ -215,6 +215,17 @@ Your job might be queued behind other jobs on the system. Training your model ca
 
 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.
 
+## Pause and resume
+
+You can track progress in both fine-tuning views of the AI Foundry portal. You'll see your job go through the same statuses as normal fine tuning jobs (queued, running, succeeded).
+
+You can also review the results files while training runs, to get a peak at the progress and whether your training is proceeding as expected.
+
+> [!NOTE]
+> During the training you can view the logs and metrics and pause the job as needed. Pausing can be useful, if metrics aren't converging or if you feel model isn't learning at the right pace. Once the training job is paused, a deployable checkpoint will be created once safety evals are complete. This checkpoint available for you to deploy and use for inference or resume the job further to completion. Pause operation is only applicable for jobs which have been trained for at least one step and are in *Running* state.
+
+:::image type="content" source="../media/how-to/reinforcement-fine-tuning/pause.png" alt-text="Screenshot of the reinforcement fine-tuning with a running job." lightbox="../media/how-to/reinforcement-fine-tuning/pause.png":::
+
 ## Analyze your custom model
 
 Azure OpenAI attaches a result file named _results.csv_ to each fine-tuning job after it completes. You can use the result file to analyze the training and validation performance of your custom model. The file ID for the result file is listed for each custom model in the **Result file Id** column on the **Models** pane for Azure AI Foundry portal. You can use the file ID to identify and download the result file from the **Data files** pane of Azure AI Foundry portal.