mlx.nn.Module.freeze#

Module.freeze(*, recurse: bool = True, keys: str | List[str] | None = None, strict: bool = False) → Module#

Freeze the Module’s parameters or some of them. Freezing a parameter means not computing gradients for it.

This function is idempotent i.e. freezing a frozen model is a no-op.

Example

For instance to only train the attention parameters from a Transformer:

model = nn.Transformer()
model.freeze()
model.apply_to_modules(lambda k, v: v.unfreeze() if k.endswith("attention") else None)
Parameters:

  • recurse (bool, optional) – If True then freeze the parameters of the submodules as well. Default: True.

  • keys (str or list[str], optional) – If provided then only these parameters will be frozen otherwise all the parameters of a module. For instance freeze all biases by calling module.freeze(keys="bias").

  • strict (bool, optional) – If set to True validate that the passed keys exist. Default: False.

Returns:

The module instance after freezing the parameters.