General purpose transformer architecture has really "transformed" the AI landscape. Learn about its origins and structure, and see it built from scratch! We’ll walk through building a small transformer on JAX, using Flax NNX to build the model architecture, Optax for loss function and optimizer creation, and training on accelerated hardware with the help of Orbax and XLA. Get a taste of development on JAX, and prepare to take your own next steps in building and training AI models. Resources: Colab notebook - Transformers workshop → https://goo.gle/44w7bI9 Kaggle notebook - Transformers workshop → https://goo.gle/4dc12mS What is JAX → https://goo.gle/4j6UQ0G "Attention is All You Need" research paper → https://goo.gle/3Z1a7ZZ Open Web Text dataset → https://goo.gle/3GKsUSM The Illustrated Transformer → https://goo.gle/452N0BP All the Transformer Math You Need to Know → https://goo.gle/4m0iFKe JAX docs → https://goo.gle/452mUyL JAX AI Stack → https://goo.gle/3GMHPvH Colab Notebooks → https://goo.gle/4m7JZpR Tips for using TPUs on Kaggle → https://goo.gle/4maY8CU Speaker: Yufeng Guo Check out all the keynote sessions from Google I/O 2025 → https://goo.gle/io25-keynote-sessions Check out the AI session track from Google I/O 2025 → https://goo.gle/io25-ai-yt Check out all of the sessions from Google I/O 2025→ https://goo.gle/io25-sessions-yt Subscribe to Google for Developers → https://goo.gle/developers Event: Google I/O 2025 Products Mentioned: AI/Machine Learning