Skip to content

Updated fine-tune and guardrail labs #155

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
ivorb merged 2 commits into from
Mar 10, 2026
Merged

Updated fine-tune and guardrail labs #155

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
382076c
Updated fine-tune and guardrail labs
GraemeMalcolm Mar 5, 2026
b6c3947
Update 04b-finetune-model.md
GraemeMalcolm Mar 6, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
112 changes: 47 additions & 65 deletions Instructions/Exercises/04b-finetune-model.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ lab:
title: 'Fine-tune a language model'
description: 'Learn how to use your own training data to fine-tune a model and customize its behavior.'
level: 300
duration: 60
duration: 90
---

# Fine-tune a language model
Expand All @@ -14,7 +14,7 @@ In this exercise, you'll fine-tune a language model with Microsoft Foundry that

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\*.
This exercise will take approximately **90** 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 <u>long</u> time to complete, and require patience. If things are taking a while, consider reviewing the [Microsoft Foundry fine-tuning documentation](https://learn.microsoft.com/azure/ai-foundry/openai/how-to/fine-tuning?view=foundry) 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.

Expand All @@ -26,89 +26,81 @@ To complete this exercise, you need:

## Create a Microsoft Foundry project

Let's start by creating a project and deploying a model.
Microsoft Foundry uses projects to organize models, resources, data, and other assets used to develop an AI solution.

1. In a web browser, open the [Microsoft Foundry portal](https://ai.azure.com) at `https://ai.azure.com` and sign in using your Azure credentials.
1. Select the project name in the upper-left corner, and then select **Create new project**.
1. Enter a valid name for your project and select **Advanced options** to configure:
- **Foundry resource**: *Autofilled based on project name*
1. In a web browser, open the [Microsoft Foundry portal](https://ai.azure.com) at `https://ai.azure.com` and sign in using your Azure credentials. Close any tips or quick start panes that are opened the first time you sign in, and if necessary use the Foundry logo at the top left to navigate to the home page.

1. If it is not already enabled, in the tool bar the top of the page, enable the **New Foundry** option. Then, if prompted, create a new project with a unique name; expanding the **Advanced options** area to specify the following settings for your project:
- **Foundry resource**: *Use the default name for your resource (usually {project_name}-resource)*
- **Subscription**: *Your Azure subscription*
- **Resource group**: *Create or select a resource group*
- **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](https://learn.microsoft.com/azure/ai-foundry/foundry-models/concepts/models-sold-directly-by-azure#fine-tuning-models) for the latest region availability.
> \* At the time of writing, these regions support fine-tuning for gpt-4.1 models. Check the [models page](https://learn.microsoft.com/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure?&pivots=azure-openai#fine-tuning-models) for the latest region availability.

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

## 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.
1. Select **Models**.
1. Search for **gpt-4.1**.
1. Select the **gpt-4.1** model, and then select **Deploy** > **Default settings** to add it to your project.

> <font color="red"><b>IMPORTANT</b>:</font> 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.
Now deploy a model that you'll use to get a performance baseline.

1. Note the deployment name (for example, `gpt-4.1`). You can confirm this by viewing the deployment in the **Models + endpoints** page.
1. On the project home page, in the **Start building** menu, select **Browse models**.
1. In the model catalog, search for `gpt-4.1`.
1. Review the model card, and then deploy it using the default settings.
1. When the model has been deployed, it will open in the model playground.

## 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](https://raw.githubusercontent.com/MicrosoftLearning/mslearn-ai-studio/refs/heads/main/data/travel-finetune-hotel.jsonl) 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.
1. Download the [training dataset](https://microsoftlearning.github.io/mslearn-ai-studio/data/travel-finetune-hotel.jsonl) at `https://microsoftlearning.github.io/mslearn-ai-studio/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.

1. In the left pane, select **Fine-tuning**.
1. Select the **Fine-tune** button at the upper right.
1. 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
1. In the Foundry portal, while viewing the model playground, left navigation pane, select **Fine-tune**.
1. Select the **Fine-tune** button at the upper right, and then configure the fine-tuning job with the following settings:
- **Base model**: Select **gpt-4.1**
- **Customization method**: Supervised
- **Training type**: Standard
- **Training data**: 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*
1. 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.
> **Note**: Fine-tuning and deployment can take a significant amount of time (60 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.
While you wait for the fine-tuning job to complete, let's chat with a *gpt-4.1* foundation model to assess how it performs.

1. In the left pane, select **Playgrounds** and then open the **Chat playground**.
1. In the chat playground, ensure that your **gpt-4.1** base model is selected.
1. In the chat window, enter the query `What can you do?` and view the response.
1. In the left pane, select **Models** and then select the **gpt-4.1** base model you deployed previously.
1. In the chat pane, enter the prompt `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.

1. In the **System message** field, enter the following prompt:
1. Change the model **Instructions** to the following prompt:

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

1. Select **Apply changes** to update the system message.
1. 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.

1. In the **System message** field, enter a new prompt:
1. In the **Instructions** 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.
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.
```

1. Select **Apply changes** to update the system message.
1. 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:
1. Continue testing the model to review its behavior. For example, ask the following questions and note 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?`

Expand Down Expand Up @@ -137,33 +129,25 @@ The base model seems to work well enough, but you may be looking for a particula

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.

1. Select the fine-tuning job to open its details page. Then, select the **Monitor** tab and explore the fine-tune metrics.
1. Select **Deploy** to deploy the fine-tuned model, and configure your deployment settings.

1. 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.
When your fine-tuned model is ready, 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**.
1. Ensure the **System message** includes these instructions:
1. In the pane on the left, select **Fine-tune** and review the status of the fine-tuning job you started earlier.
1. Select the job to view its details. You can use the **Logs** tab to review the fine-tuning tasks that have been performed so far.
1. When fine-tuning is complete, and the model has been automatically deployed, view the **Models** page to verify that it is listed.

> **Tip**: If automatic deployment fails, select the completed fine-tuning job and deploy the model from there.
1. Select the fine-tuned model to open it in the model playground.
1. Update the **Instructions** to be the same as you tested with the base model:

```
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.
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.
```

1. 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:
1. Test your fine-tuned model to assess whether its behavior is more consistent than the base model. For example, ask the following questions again and explore the model's answers:

`Where in Rome should I stay?`

Expand All @@ -175,11 +159,9 @@ Now that you deployed your fine-tuned model, you can test it like you tested you

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

1. 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.
If you've finished exploring Microsoft Foundry, you should delete the resources you have created in this exercise to avoid incurring unnecessary Azure costs.

1. Open the [Azure portal](https://portal.azure.com) and view the contents of the resource group where you deployed the resources used in this exercise.
1. On the toolbar, select **Delete resource group**.
Expand Down
Loading