Add sample CLI section for fine-tuning

Author: ssalgadodev

articles/ai-studio/how-to/fine-tune-serverless.md

@@ -90,6 +90,7 @@ The supported file type is JSON Lines. Files are uploaded to the default datasto
 The training and validation data you use **must** be formatted as a JSON Lines (JSONL) document in which each line represents a single prompt-completion pair.
 
 ### Example file format
+
 ```json
 {"prompt": "<prompt text>", "completion": "<ideal generated text>"}
 {"prompt": "<prompt text>", "completion": "<ideal generated text>"}
@@ -188,6 +189,16 @@ For more information on how to track costs, see [Monitor costs for models offere
 
 You can use this [sample notebook](https://github.com/Azure/azureml-examples/blob/main/sdk/python/jobs/finetuning/standalone/chat-completion/chat_completion_with_model_as_service.ipynb)  to create a standalone fine-tuning job to enhance a model's ability to summarize dialogues between two people using the Samsum dataset. The training data utilized is the ultrachat_200k dataset, which is divided into four splits suitable for supervised fine-tuning (sft) and generation ranking (gen). The notebook employs the available Azure AI models for the chat-completion task (If you would like to use a different model than what's used in the notebook, you can replace the model name). The notebook includes setting up prerequisites, selecting a model to fine-tune, creating training and validation datasets, configuring and submitting the fine-tuning job, and finally, creating a serverless deployment using the fine-tuned model for sample inference.
 
+## Sample CLI
+
+Additionally, you can use this sample CLI to create a standalone fine-tuning job to enhance a model's ability to summarize dialogues between two people using a dataset. 
+
+::: zone pivot="programming-language-cli"
+[!INCLUDE [cli](cli/jobs/finetuning/standalone/model-as-a-service/chat-completion/chat-completion-finetuning.yaml)] 
+::: zone-end
+
+The training data used is the same as demonstrated in the SDK notebook. The CLI employs the available Azure AI models for the chat-completion task. If you prefer to use a different model than the one in the CLI sample, you can update the arguments, such as 'model path,' accordingly
+
 ## Content filtering
 
 Models deployed as a service with pay-as-you-go billing are protected by Azure AI Content Safety. When deployed to real-time endpoints, you can opt out of this capability. With Azure AI content safety enabled, both the prompt and completion pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Learn more about [Azure AI Content Safety](../concepts/content-filtering.md).