mlx-optimizers - 0.4.1 documentation#

A library to experiment with new optimization algorithms in MLX.

Diverse Exploration: includes proven and experimental optimizers like DiffGrad, QHAdam, and others.
Easy Integration: compatible with MLX for straightforward experimentation and downstream adoption.
Benchmark Examples: enables quick testing on classic optimization and machine learning tasks.

See a full list of optimizers in the API Reference.

Example Usage#

   import mlx_optimizers as optim

   #... model, grads, etc.
   optimizer = optim.DiffGrad(learning_rate=0.001)
   optimizer.update(model, grads)

Install

API Reference

Development