Fine-Tune GPT-3.5-Turbo in Azure for Smarter AI Responses — Part 2

In Part 1, we discussed the importance of fine-tuning GPT-3.5-Turbo and how to prepare your dataset for the fine-tuning process. Now, let’s dive into the actual fine-tuning process, including uploading the dataset, starting the fine-tuning job, and deploying your custom model in Azure

Step 1: Uploading the Dataset to Azure Data Files

Before we can start the fine-tuning process, we need to upload our prepared dataset to Azure Data Files. Follow these steps to upload your dataset:

Navigate to Azure OpenAI Studio:

• Open Azure OpenAI Studio and go to the Data Files section.

Upload the Dataset:

• Upload your dataset.jsonl file to the Data Files section.

Step 2: Starting the Fine-Tuning Job

With your dataset uploaded to Azure Data Files, you can now start the fine-tuning job in Azure OpenAI Studio. Follow these steps:

Navigate to Azure OpenAI Studio:

• Open Azure OpenAI Studio and go to the Fine-Tuning section.

Create a Fine-Tuning Job:

• Click on “Create Fine-Tuning Job” and fill in the required details:
• Model: Select gpt-35-turbo.

Select Training Data:

• Choose the training data from the Data Files section, selecting the dataset.jsonl file you uploaded.

Select Validation Data:

• For this example, we will not use validation data. Select “None” for validation data.

Configure Task Parameters:

• Configure the task parameters. For simplicity, we will keep the default settings for Batch Size, Learning Rate Multiplier, Number of Epochs, and Seed.

Default Task Parameters:

• Batch Size: This parameter determines the number of training examples utilized in one iteration. A larger batch size can speed up training but requires more memory. The default setting balances performance and resource usage.
• Learning Rate Multiplier: This parameter scales the learning rate, which controls how much to change the model in response to the estimated error each time the model weights are updated. The default multiplier ensures stable learning without drastic changes.
• Number of Epochs: An epoch is one complete pass through the entire training dataset. The default number of epochs ensures the model has sufficient exposure to the training data to learn effectively without overfitting.
• Seed: This parameter sets the random seed for reproducibility. Using a fixed seed ensures that the fine-tuning process can be replicated with the same results.

Start the Job:

• Once you have configured the job parameters, click “Start Job” to begin the fine-tuning process. The job may take some time to complete, depending on the size of your dataset and the complexity of the model.

Step 3: Deploying Your Custom Model

After the fine-tuning job is complete, you can deploy your custom model for use in your applications. Follow these steps:

Navigate to the Deployments Section:

• In Azure OpenAI Studio, go to the Deployments section.

Create a New Deployment:

• Click on “Create Deployment” and select your fine-tuned model from the list of available models.

Deploy the Model:

• Click “Deploy” to deploy your custom model. Once deployed, you will receive an endpoint and API key that you can use to interact with your fine-tuned model.

Testing and Using Your Fine-Tuned Model

With your custom model deployed, you can now test it and integrate it into your applications. Use the provided endpoint and API key to send requests to your fine-tuned model and evaluate its performance.

Conclusion

Fine-tuning GPT-3.5-Turbo in Azure allows you to tailor the model to your specific business needs, improving the relevance and accuracy of its responses. By following the steps outlined in this article, you can successfully fine-tune and deploy a custom model that meets your requirements.

Stay tuned for more articles on advanced AI techniques and best practices!