Fine-tune a language model

When you want a language model to behave a certain way, you can use prompt engineering to define the desired behavior. When you want to improve the consistency of the desired behavior, you can opt to fine-tune a model, comparing it to your prompt engineering approach to evaluate which method best fits your needs.

In this exercise, you’ll fine-tune a language model with Microsoft Foundry that you want to use for a custom chat application scenario. You’ll compare the fine-tuned model with a base model to assess whether the fine-tuned model fits your needs better.

Imagine you work for a travel agency and you’re developing a chat application to help people plan their vacations. The goal is to create a simple and inspiring chat that suggests destinations and activities with a consistent, friendly conversational tone.

This exercise will take approximately 60 minutes*.

* Note: This timing is an estimate based on the average experience. Fine-tuning is dependent on cloud infrastructure resources, which can take a variable amount of time to provision depending on data center capacity and concurrent demand. Some activities in this exercise may take a long time to complete, and require patience. If things are taking a while, consider reviewing the Microsoft Foundry fine-tuning documentation or taking a break. It is possible some processes may time-out or appear to run indefinitely. Some of the technologies used in this exercise are in preview or in active development. You may experience some unexpected behavior, warnings, or errors.

Prerequisites

To complete this exercise, you need:

• An Azure subscription with permissions to create AI resources.

Create a Microsoft Foundry project

Let’s start by creating a project and deploying a model.

1. In a web browser, open the Microsoft Foundry portal at https://ai.azure.com and sign in using your Azure credentials.
2. Select the project name in the upper-left corner, and then select Create new project.
3. Enter a valid name for your project and select Advanced options to configure:
   • Foundry resource: Autofilled based on project name
   • Region: Select one of the following regions:*
     • North Central US
     • Sweden Central
   • Subscription: Select your subscription
   • Resource group: Create a new resource group or select an existing one

   * At the time of writing, these regions support fine-tuning for gpt-4.1 models. Check the models page for the latest region availability.

4. Select Create and wait for it to be created. When the project overview page appears, your project is ready.

Deploy a model

Now deploy a base gpt-4.1 model that you’ll test and compare with a fine-tuned version.

1. In your project, select Discover in the upper-right navigation.
2. Select Models.
3. Search for gpt-4.1.

4. Select the gpt-4.1 model, and then select Deploy > Default settings to add it to your project.

   IMPORTANT:

   Depending on your available quota for gpt-4.1 models you might receive an additional prompt to deploy the model to a resource in a different region. If this happens, do so using the default settings.

5. Note the deployment name (for example, gpt-4.1). You can confirm this by viewing the deployment in the Models + endpoints page.

Fine-tune a model

Because fine-tuning a model takes some time to complete, you’ll start the fine-tuning job now and come back to it after exploring the base gpt-4.1 model you already deployed.

1. Download the training dataset at https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-studio/refs/heads/main/data/travel-finetune-hotel.jsonl and save it as a JSONL file locally.

   Note: Your device might default to saving the file as a .txt file. Select all files and remove the .txt suffix to ensure you’re saving the file as JSONL.

2. In the left pane, select Fine-tuning.
3. Select the Fine-tune button at the upper right.
4. Configure the fine-tuning job with the following settings:
   • Base model: Select gpt-4.1
   • Customization method: Supervised fine-tuning
   • Training type: Global (or Standard if you need data residency)
   • Training data: For Data source, select Upload new dataset and upload the .jsonl file you downloaded previously
   • Suffix: ft-travel
   • Automatically deploy model after job completion: Selected
   • Deployment type: Developer
   • Leave the remaining hyperparameters at their defaults
5. Select Submit to start the fine-tuning job. It may take some time to complete. You can continue with the next section of the exercise while you wait.

Note: Fine-tuning and deployment can take a significant amount of time (30 minutes or longer), so you may need to check back periodically. You can see more details of the progress so far by selecting the fine-tuning job and viewing its Monitor tab.

Chat with a base model

While you wait for the fine-tuning job to complete, let’s chat with a base gpt-4.1 model to assess how it performs.

1. In the left pane, select Playgrounds and then open the Chat playground.
2. In the chat playground, ensure that your gpt-4.1 base model is selected.

3. In the chat window, enter the query What can you do? and view the response.

   The answers may be fairly generic. Remember we want to create a chat application that inspires people to travel.

4. In the System message field, enter the following prompt:

    You are an AI assistant that helps people plan their travel.
   

5. Select Apply changes to update the system message.

6. In the chat window, enter the query What can you do? again, and view the response.

   As a response, the assistant may tell you that it can help you book flights, hotels and rental cars for your trip. You want to avoid this behavior.

7. In the System message field, enter a new prompt:

    You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms.
    You should not provide any hotel, flight, rental car or restaurant recommendations.
    Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday.
   

8. Select Apply changes to update the system message.

9. Continue testing your chat application to verify it doesn’t provide any information that isn’t grounded in retrieved data. For example, ask the following questions and review the model’s answers, paying particular attention to the tone and writing style that the model uses to respond:

   Where in Rome should I stay?

   I'm mostly there for the food. Where should I stay to be within walking distance of affordable restaurants?

   What are some local delicacies I should try?

   When is the best time of year to visit in terms of the weather?

   What's the best way to get around the city?

Review the training file

The base model seems to work well enough, but you may be looking for a particular conversational style from your generative AI app. The training data used for fine-tuning offers you the chance to create explicit examples of the kinds of response you want.

1. Open the JSONL file you downloaded previously (you can open it in any text editor)

2. Examine the list of the JSON documents in the training data file. The first one should be similar to this (formatted for readability):

    {"messages": [
        {"role": "system", "content": "You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms. You should not provide any hotel, flight, rental car or restaurant recommendations. Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday."},
        {"role": "user", "content": "What's a must-see in Paris?"},
        {"role": "assistant", "content": "Oh la la! You simply must twirl around the Eiffel Tower and snap a chic selfie! After that, consider visiting the Louvre Museum to see the Mona Lisa and other masterpieces. What type of attractions are you most interested in?"}
        ]}
   

   Each example interaction in the list includes the same system message you tested with the base model, a user prompt related to a travel query, and a response. The style of the responses in the training data will help the fine-tuned model learn how it should respond.

Deploy the fine-tuned model

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

1. In the left pane, select Fine-tuning to find your fine-tuning job and its status. If it’s still running, you can opt to continue chatting with your deployed base model or take a break. If it’s completed, you can continue.

   Tip: Use the Refresh button in the fine-tuning page to refresh the view. If the fine-tuning job disappears entirely, refresh the page in the browser.

2. Select the fine-tuning job to open its details page. Then, select the Monitor tab and explore the fine-tune metrics.

3. Select Deploy to deploy the fine-tuned model, and configure your deployment settings.

4. Wait for the deployment to be complete before you can test it, this might take a while. You may need to refresh the browser to see the updated status.

Test the fine-tuned model

Now that you deployed your fine-tuned model, you can test it like you tested your deployed base model.

1. When the deployment is ready, navigate to the fine-tuned model and select Open in playground.

2. Ensure the System message includes these instructions:

    You are an AI travel assistant that helps people plan their trips. Your objective is to offer support for travel-related inquiries, such as visa requirements, weather forecasts, local attractions, and cultural norms.
    You should not provide any hotel, flight, rental car or restaurant recommendations.
    Ask engaging questions to help someone plan their trip and think about what they want to do on their holiday.
   

3. Test your fine-tuned model to assess whether its behavior is more consistent now. For example, ask the following questions again and explore the model’s answers:

   Where in Rome should I stay?

   I'm mostly there for the food. Where should I stay to be within walking distance of affordable restaurants?

   What are some local delicacies I should try?

   When is the best time of year to visit in terms of the weather?

   What's the best way to get around the city?

4. After reviewing the responses, how do they compare to those of the base model?

Clean up

If you’ve finished exploring the Microsoft Foundry portal, you should delete the resources you have created in this exercise to avoid incurring unnecessary Azure costs.

1. Open the Azure portal and view the contents of the resource group where you deployed the resources used in this exercise.
2. On the toolbar, select Delete resource group.
3. Enter the resource group name and confirm that you want to delete it.