Flax NNX

Flax is explicitly designed for flexibility, allowing researchers to modify training loops directly rather than being constrained by a rigid framework, as emphasized in the overview and philosophy.

Built in close collaboration with the JAX team, Flax leverages JAX's high-performance capabilities for efficient training on CPUs, GPUs, and TPUs, with seamless integration documented throughout.

Flax NNX introduces Python reference semantics, enabling models to be expressed as regular Python objects with mutability and reference sharing for easier inspection and debugging, as highlighted in the README.

Provides a full neural network API with standard layers, utilities for replicated training and checkpointing, and educational examples like MNIST and Gemma inference to accelerate research workflows.