Train a GPT2 model with JAX on TPU for free

This comprehensive guide details the process of building and pretraining a GPT-2 language model from scratch using JAX, leveraging Google TPUs for efficient computation. The tutorial is hands-on and assumes familiarity with basic machine learning concepts, providing a step-by-step approach to utilizing JAX's modular ecosystem, including Flax for neural network building, Orbax for checkpointing, and Optax for optimization. The guide explains the architecture of the GPT-2 model, focusing on the transformer block and exploring how to implement data and model parallelism using SPMD (Single Program Multiple Data) across TPU cores. It includes practical advice for setting up a TPU runtime on platforms like Colab or Kaggle, configuring a hardware mesh for parallel computation, and partitioning model parameters for potential scalability. The tutorial also covers defining training functions, utilizing JIT compilation for performance optimization, and employing AdamW optimization with a cosine decay schedule. Readers are encouraged to explore further scaling techniques and advanced LLM training, with references to additional resources for building larger models.

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