diff --git a/torchao/float8/README.md b/torchao/float8/README.md
index b9b40d7e41..34dee659f8 100644
--- a/torchao/float8/README.md
+++ b/torchao/float8/README.md
@@ -34,21 +34,12 @@ m = nn.Sequential(
 x = torch.randn(4096, 2048, device="cuda", dtype=torch.bfloat16)
 optimizer = torch.optim.SGD(m.parameters(), lr=0.1)
 
-# optional: filter modules from being eligible for float8 conversion
-def module_filter_fn(mod: torch.nn.Module, fqn: str):
-    # don't convert the last module
-    if fqn == "1":
-        return False
-    # don't convert linear modules with weight dimensions not divisible by 16
-    if isinstance(mod, torch.nn.Linear):
-        if mod.in_features % 16 != 0 or mod.out_features % 16 != 0:
-            return False
-    return True
-
-# convert specified `torch.nn.Linear` modules to `Float8Linear`
-convert_to_float8_training(m, module_filter_fn=module_filter_fn)
-
-# enable torch.compile for competitive performance
+# convert specified `torch.nn.Linear` modules to `Float8Linear`, with compute
+# and optionally distributed communications in float8
+convert_to_float8_training(m)
+
+# enable torch.compile to generate fused kernels for float8 scaling and casting,
+# which improves performance
 m = torch.compile(m)
 
 # toy training loop
@@ -94,7 +85,8 @@ config = Float8LinearConfig(
 # convert all `torch.nn.Linear` modules to `Float8Linear`, specifying custom scaling behavior
 convert_to_float8_training(m, config=config)
 
-# enable torch.compile for competitive performance
+# enable torch.compile to generate fused kernels for float8 scaling and casting,
+# which improves performance
 m = torch.compile(m)
 
 # toy training loop