Train a GPT from scratch in the browser
Karpathy's microGPT
What is Train a GPT from scratch in the browser?
Train a GPT from Scratch in the Browser is an educational tool that explain how GPT models work by allowing users to build, train, and run their own transformer-based language models entirely within a web browser. Inspired by Andrej Karpathy's microGPT project, this tool eliminates the need for local machine learning infrastructure, making deep learning accessible to beginners and students without requiring GPU resources or complex setup processes. Users can understand the fundamentals of neural networks, attention mechanisms, and language model training through hands-on experimentation. The tool is particularly valuable for educators, students, and AI enthusiasts who want to learn how transformer architectures function at a practical level rather than just theoretically.
Key Features
Browser-based training
Train models directly in your browser without installing software or accessing GPUs
Educational interface
Visual explanations and step-by-step guidance through the model training process
Customizable datasets
Upload or use sample text data to train your own personalise language model
Real-time inference
Test and interact with trained models to see predictions in action
Code transparency
Access underlying code to understand implementation details of GPT architecture
No infrastructure required
Completely free from setup complexity and hardware dependencies
Pros & Cons
Advantages
- Completely free and accessible, no GPU, credit cards, or complex setup required
- Excellent for learning, transparent implementation helps understand how GPT models actually work
- Browser-based convenience, nothing to download or install
- Hands-on experimentation, quickly iterate and modify models to see immediate effects
Limitations
- Limited scalability, browser-based training is slower and less powerful than dedicated ML infrastructure
- Small model sizes, realistic only for toy models and educational datasets, not production-grade systems
- Performance constraints, browser limitations may cause lag with larger datasets or complex architectures
Use Cases
AI education: Students learning transformer architecture and language model fundamentals
Prototyping ideas: Quick experimentation with small datasets before scaling to larger systems
Teaching tool: Instructors demonstrating GPT concepts in classrooms without infrastructure
Portfolio projects: Building AI knowledge for career transitions or skill development
Research exploration: Investigating attention mechanisms and embedding behaviour at a granular level