add usage of the meta flag to README

README.md

@@ -76,11 +76,14 @@ class MyModel(nn.Module):
 
 ### Instantiate a base model
 base_model = MyModel(width=1)
+### Optionally, use `device='meta'` to avoid instantiating the model
+### This requires you to pass the device flag down to all sub-modules
+# base_model = MyModel(width=1, device='meta')
 ### Instantiate a "delta" model that differs from the base model
 ###   in all dimensions ("widths") that one wishes to scale.
 ### Here it's simple, but e.g., in a Transformer, you may want to scale
 ###   both nhead and dhead, so the delta model should differ in both.
-delta_model = MyModel(width=2)
+delta_model = MyModel(width=2) # Optionally add the `device='meta'` to avoid instantiating
 
 ### Instantiate the target model (the model you actually want to train).
 ### This should be the same as the base model except 
@@ -123,7 +126,8 @@ optimizer = MuSGD(model.parameters(), lr=0.1)
 ```
 
 Note the base and delta models *do not need to be trained* --- we are only extracting parameter shape information from them.
-Ideally, we can do so without instantiating the model parameters at all, like in [JAX](https://github.com/google/jax), but unfortunately we currently can't do that in PyTorch --- but upvote [this PyTorch issue](https://github.com/pytorch/pytorch/issues/74143) if you want to see this feature happening!
+Therefore, optionally, we can avoid instantiating these potentially large models by passing `device='meta'` to their constructor.
+However, you need to make sure that the `device` flag is appropriately passed down to the constructor of all submodules.
 
 ## How `mup` Works Under the Hood