.. _saving_and_loading:

Saving and Loading Arrays

.. currentmodule:: mlx.core

MLX supports multiple array serialization formats.

.. list-table:: Serialization Formats :widths: 20 8 25 25 :header-rows: 1

• • Format
  • Extension
  • Function
  • Notes
• • NumPy
  • .npy
  • :func:save
  • Single arrays only
• • NumPy archive
  • .npz
  • :func:savez and :func:savez_compressed
  • Multiple arrays
• • Safetensors
  • .safetensors
  • :func:save_safetensors
  • Multiple arrays
• • GGUF
  • .gguf
  • :func:save_gguf
  • Multiple arrays

The :func:load function will load any of the supported serialization formats. It determines the format from the extensions. The output of :func:load depends on the format.

Here's an example of saving a single array to a file:

.. code-block:: shell

a = mx.array([1.0]) mx.save("array", a)

The array a will be saved in the file array.npy (notice the extension is automatically added). Including the extension is optional; if it is missing it will be added. You can load the array with:

.. code-block:: shell

mx.load("array.npy") array([1], dtype=float32)

Here's an example of saving several arrays to a single file:

.. code-block:: shell

a = mx.array([1.0]) b = mx.array([2.0]) mx.savez("arrays", a, b=b)

For compatibility with :func:numpy.savez the MLX :func:savez takes arrays as arguments. If the keywords are missing, then default names will be provided. This can be loaded with:

.. code-block:: shell

mx.load("arrays.npz") {'b': array([2], dtype=float32), 'arr_0': array([1], dtype=float32)}

In this case :func:load returns a dictionary of names to arrays.

The functions :func:save_safetensors and :func:save_gguf are similar to :func:savez, but they take as input a :obj:dict of string names to arrays:

.. code-block:: shell

a = mx.array([1.0]) b = mx.array([2.0]) mx.save_safetensors("arrays", {"a": a, "b": b})