In the rapidly evolving landscape of artificial intelligence, foundation models—large pre-trained systems like GPT-4, BERT, or Vision Transformers, have reshaped how developers approach machine learning tasks. These models, trained on vast datasets spanning text, images, or multimodal inputs, capture broad patterns and knowledge.
But how do you get this model to understand domain-specific needs? This is where fine-tuning, a process that adapts these general-purpose models to excel at specialized tasks comes in. Whether summarizing legal documents or analyzing sentiment in customer feedback, fine-tuning bridges the gap between a model’s generic capabilities and domain-specific needs.
In this article, we will go over how to fine-tune foundation models using Vertex AI. We will go through the steps, from setting up your environment and preparing your data to deploying your fine-tuned model for inference.
What is Fine Tuning?
Fine-tuning is a machine learning technique that involves taking a pre-trained model and adapting it to perform a specific task. This process uses the knowledge the existing model has already acquired during its initial training on a large dataset, allowing it to be reused for a new, related task with potentially limited data. By adjusting the model's parameters using task-specific data, fine-tuning enables the model to specialize in the desired application, enhancing its performance and efficiency.
In practice, fine-tuning is valuable when resources for training a model from scratch are constrained. It allows developers to build upon existing models, saving time and computational power while achieving high accuracy in specialized tasks. This approach is widely used across various domains, including natural language processing and computer vision.
Why Vertex AI for fine-tuning?
Vertex AI is a Google Cloud platform that helps developers and data scientists build, train, and deploy machine learning models all in one place. It offers tools and resources to make working with AI easier and more efficient, whether you are starting from scratch or fine-tuning an existing model.
One of the main reasons to use Vertex AI for fine-tuning a model is its unified environment. Instead of jumping between different tools for data preparation, model training, and deployment, you can do everything within Vertex AI. This setup not only saves time but also reduces the chances of errors when moving data or models between different systems.
Another great feature of Vertex AI is its scalability. Whether you are working on a small project or handling large amounts of data, Vertex AI can handle it. It runs on Google's infrastructure, meaning you can scale up your processing power as needed without worrying about the underlying hardware.
Vertex AI also offers pre-built models and AutoML tools, which can be really handy if you don't want to build a model from scratch. You can take a pre-trained model and fine-tune it for your specific needs, like customizing a language model for sentiment analysis or adapting a vision model for specific image classification tasks.
Fine Tuning a Model on Vertex AI
In this section, we'll explore how to fine-tune a foundation model using Vertex AI's Model Garden.
Selecting a Pre-trained Model
Before we proceed, we will pick a model that will be used. For this tutorial, we are using the IMDb Movie Reviews Dataset. We will be using the Gemma 2 model in the Model Garden.
Prepare The Dataset
Preparing the dataset is the next step in fine-tuning a machine learning model. It involves gathering relevant data, cleaning it to remove errors or inconsistencies, and organizing it into a structured format suitable for training. This process ensures that the model learns effectively from accurate and representative examples, leading to better performance on the intended task.
To include the data, we will have to create a bucket in the “Output directory”. Follow the steps and create a bucket to uplaod the dataset that will be needed.
After creating the bucket, upload the jsonl files to the bucket directory. Search for “bucket” in the search bar and navigate to the “Cloud Storage” service. To convert the txt files to jsonl , I wrote this simple script to follow the file format from the example. Then merge all the pos and neg into a single jsonl file (e.g. the pos and neg folders under the train folder will all be merged into a general train.jsonl file ).
Initiate the Fine-Tuning Process
After passing in the remaining parameters and have clicked on “Start tuning”, the process of tuning will begin and it is possible to view and track the progress.
After the fine-tuning process is done, you should be notified via email. Unfortunately for me, my model took days to train and I even cancelled it.
Deploy the Fine-Tuned Model (Optional)
Once you’re satisfied with the model's performance, deploy it to an endpoint making it accessible for inference in your applications.
Conclusion
In this tutorial, we've explored the meaning and process of fine-tuning foundation models using Vertex AI, demonstrating how to adapt pre-trained models to specific tasks such as sentiment analysis with the IMDb Movie Reviews Dataset.
By following the steps outlined in this tutorial, you can effectively fine-tune foundation models to meet your unique requirements, harnessing the power of Vertex AI to deliver robust and customized AI solutions.
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