Train a GPT2 model with JAX on TPU for free- Google Developers Blog

This post provides a hands-on tutorial for building and pretraining a GPT-2 model using JAX on Google TPUs. It details the use of the JAX ecosystem (Flax, Orbax, Optax) for model definition, including the implementation of a Transformer block with NNX. It explains how to leverage SPMD for parallel computation across TPU cores and implement data parallelism. The post also covers setting up a hardware mesh, partitioning model parameters with `nnx.with_partitioning_`, defining JIT-compiled loss and training step functions, and using an AdamW optimizer with a cosine decay schedule. It demonstrates data partitioning using `jax.device_put` and mentions the use of Weights and Biases for tracking training progress.