flax model

Flax is a popular deep learning library built on top of JAX, a high-performance scientific computing library for Python. It provides an easy-to-use API for defining and training neural network models, while leveraging the speed and efficiency of JAX's Just-In-Time (JIT) compilation and automatic differentiation.

In the context of Flax, a model typically refers to a class or a set of functions that define the architecture of a neural network. It includes layers, activation functions, and parameters that are learned during training. Flax supports various types of models, such as feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and more.

Here are some key aspects of the Flax Model:

1. **Structured State**: Flax uses a structured state format, where all learnable parameters are stored in a single object, making it easier to manage and apply weight updates.

2. **Functional API**: The library encourages functional programming style, allowing users to create complex models using compositions of simple functions, which makes code more modular and testable.

3. **Module System**: Flax uses a hierarchical module system that allows you to create and reuse sub-modules, enabling code reusability and organization.

4. **Modularity**: Models are composed of individual modules, each with their own forward pass function, making it simple to experiment with different architectures.

5. **Dynamic Shapes**: Flax handles variable-size inputs and dynamic shapes efficiently, which is crucial for sequence modeling tasks.