Training Your Own AI Model Is Not As Hard As You (Probably) Think
People & Blogs
Introduction
Training an AI model tailored to your specific needs isn’t as daunting as you might imagine. In this article, we’ll explore a step-by-step process that demonstrates how training a specialized AI model can yield faster, cheaper, and better results compared to relying on large off-the-shelf models like those offered by OpenAI.
Why Not Just Use a Pre-existing LLM?
When embarking on our AI project, we first attempted to utilize existing large language models (LLMs), such as OpenAI's GPT-3 and GPT-4. However, our experience showed that these models were not effective for our specific use case. The results were slow, expensive, and often unpredictable, making customization particularly challenging. This prompted us to train our own model.
Breaking Down the Problem
The first step in our journey was to dissect our primary challenge: converting Figma designs into high-quality code. While we began by testing pre-existing models, we discovered that they did not effectively address our needs—even when using raw JSON data or images from Figma designs.
Given the complexity of our problem, we realized that building a single, large model to handle everything was not practical. Training such models is cost-intensive and time-consuming, and it is often difficult to procure the vast amounts of data required for effective training.
Instead, we pivoted our strategy by breaking our problem down into smaller, manageable components that could be solved with traditional coding methods. In doing so, we found that we could resolve certain issues without AI, thus reducing complexity.
Identify the Right Type of Model
To tackle our specific problem, we recognized the need for an object detection model, which could analyze Figma designs as images and return bounding boxes to indicate where specific elements were located. We generated extensive example data as training material for this model.
Generating Example Data
We sought public sources of data to avoid the need to manually code vast numbers of Figma designs into React. Utilizing a web crawler, we dynamically identified images on various web pages, creating a dataset to train our object detection model.
However, the importance of data quality cannot be overstated. We meticulously verified and corrected our dataset to ensure that it provided reliable input for model training.
Training Your Model
For the training process, we utilized Google’s Vertex AI. The platform has built-in tools that simplify dataset management and model training, so we didn't need to code everything from scratch.
After we prepared our dataset, we uploaded it to Vertex AI, where we configured an object detection model with minimal effort. Training proceeded efficiently and, importantly, at a lower cost than maintaining our own GPU.
Once the training phase was complete, we deployed the model and were able to create an API that could return bounding boxes as expected.
Bringing It All Together
In our project, we also created specialized models for layout hierarchy and code generation. The final piece of our puzzle involved incorporating an LLM for making modifications to the generated code. While we still found this step slower and more costly, the LLM ultimately served as a valuable tool for refining the code.
By assembling these components into a cohesive toolchain, we built a system where users can input a design and receive responsive, pixel-perfect code outputs that are easily transferable into their codebases.
Conclusion
In summary, while using an existing LLM is worth exploring, we recommend maximizing traditional coding wherever possible for efficiency and predictability. When facing challenges, don’t hesitate to train specialized models using platforms like Google Vertex AI, allowing you to build powerful, tailored solutions for your use case.
Keywords
- AI Model
- Large Language Models (LLMs)
- Object Detection
- Figma Designs
- Data Quality
- Google Vertex AI
- Dataset Generation
- Code Generation
- Responsive Design
FAQ
Q1: Why should I not just use a pre-existing LLM?
A1: Existing LLMs can be slow, expensive, and difficult to customize for specific use cases. Training your own specialized model can provide faster and more predictable results.
Q2: What is the first step in training my own AI model?
A2: The first step involves breaking down your problem into smaller, manageable components and exploring whether existing models can solve any part of the challenge.
Q3: How can I generate example data for training my model?
A3: You can harness tools like web crawlers to gather publicly available data and generate training datasets dynamically.
Q4: What platform can I use for training my model?
A4: Google Vertex AI offers efficient tools for data management and model training, simplifying the process of deploying your specialized AI models.
Q5: Is it feasible to train large models?
A5: Training large models is often costly and time-consuming. Instead, focusing on smaller, specialized models can improve efficiency and results.
