Fine-tune a language model for chat completion in the Azure AI Studio

In this exercise, you’ll fine-tune a language model with the Azure AI Studio that you want to use for a custom copilot scenario.

This exercise will take approximately 45 minutes.

Create an AI hub and project in the Azure AI Studio

You start by creating an Azure AI Studio project within an Azure AI hub:

In a web browser, open https://ai.azure.com and sign in using your Azure credentials.
Select the Home page, then select + New project.
In the Create a new project wizard, create a project with the following settings:
- Project name: A unique name for your project
- Hub: Create a new hub with the following settings:
  - Hub name: A unique name
  - Subscription: Your Azure subscription
  - Resource group: A new resource group
  - Location: Make a random choice from any of the following regions*
  - East US 2
  - North Central US
  - Sweden Central
  - Switzerland North
- Connect Azure AI Services or Azure OpenAI: Create a new connection
- Connect Azure AI Search: Skip connecting
* Azure OpenAI resources are constrained at the tenant level by regional quotas. The listed regions include default quota for the model type(s) used in this exercise. Randomly choosing a region reduces the risk of a single region reaching its quota limit. In the event of a quota limit being reached later in the exercise, there’s a possibility you may need to create another resource in a different region. Learn more about model availability per region
Review your configuration and create your project.
Wait for your project to be created.

Before you can fine-tune a model, you need a dataset.

Save the training dataset as JSONL file locally: https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-studio/main/data/travel-finetune.jsonl
Navigate to the Fine-tuning page under the Tools section, using the menu on the left.
Select the button to add a new fine-tune model, select the gpt-35-turbo model, and select Confirm.
Fine-tune the model using the following configuration:
- Model version: Select the default version
- Model suffix: ft-travel
- Azure OpenAI connection: Select the connection that was created when you created your hub
- Training data: Upload files
- Upload file: Select the JSONL file you downloaded in a previous step.
Tip: You don’t have to wait for the data processing to be completed to continue to the next step.
- Validation data: None
- Task parameters: Keep the default settings
Finetuning will start and may take some time to complete.

Note: Fine-tuning and deployment can take some time, so you may need to check back periodically to complete the next step.

When fine-tuning has successfully completed, you can deploy the model.

Select the fine-tuned model. Select the Metrics tab and explore the fine-tune metrics.
Deploy the fine-tuned model with the following configurations:
- Deployment name: A unique name for your model, you can use the default
- Deployment type: Standard
- Tokens per Minute Rate Limit (thousands): 5K
- Content filter: Default

Now that you deployed your fine-tuned model, you can test the model like you can test any other deployed model.

When the deployment is ready, navigate to the fine-tuned model and select Open in playground.
In the chat window, enter the query What can you do? Notice that even though you didn’t specify the system message to instruct your model to answer travel-related questions, the model already understands what it should focus on.
Try with another query like Where should I go on holiday for my 30th birthday?

When you finish exploring the Azure AI Studio, you should delete the resources you’ve created to avoid unnecessary Azure costs.

Navigate to the Azure portal at https://portal.azure.com.
In the Azure portal, on the Home page, select Resource groups.
Select the resource group that you created for this exercise.
At the top of the Overview page for your resource group, select Delete resource group.
Enter the resource group name to confirm you want to delete it, and select Delete.