diff --git a/scripts/base_train.py b/scripts/base_train.py
index bb76e90..24091b6 100644
--- a/scripts/base_train.py
+++ b/scripts/base_train.py
@@ -170,20 +170,22 @@ if args.fp8:
         # from torchao.float8 import Float8LinearConfig, convert_to_float8_training
         import torch.nn as nn
 
-        # Filter: only convert layers with dimensions divisible by 16 (FP8 hardware requirement)
+        # Filter: dims must be divisible by 16 (FP8 hardware requirement) large enough
         def fp8_module_filter(mod: nn.Module, fqn: str) -> bool:
             if not isinstance(mod, nn.Linear):
                 return False
-            # FP8 requires both in_features and out_features divisible by 16
             if mod.in_features % 16 != 0 or mod.out_features % 16 != 0:
                 return False
+            if min(mod.in_features, mod.out_features) < 128:
+                return False
             return True
 
         fp8_config = Float8LinearConfig.from_recipe_name(args.fp8_recipe)
+        num_linear = sum(1 for m in model.modules() if isinstance(m, nn.Linear))
         convert_to_float8_training(model, config=fp8_config, module_filter_fn=fp8_module_filter)
-        num_fp8_layers = sum(1 for m in model.modules() if 'Float8' in type(m).__name__)
-        num_skipped = sum(1 for m in model.modules() if isinstance(m, nn.Linear)) - num_fp8_layers
-        print0(f"✓ FP8 training enabled ({args.fp8_recipe} scaling) - converted {num_fp8_layers} layers, skipped {num_skipped} (dims not divisible by 16)")
+        num_fp8 = sum(1 for m in model.modules() if 'Float8' in type(m).__name__)
+        num_skipped = num_linear - num_fp8
+        print0(f"✓ FP8 training enabled ({args.fp8_recipe} scaling) - converted {num_fp8}/{num_linear} linear layers, skipped {num_skipped} (too small)")
 
 # Context manager to temporarily disable FP8 so that model evaluation remains in BF16
 @contextmanager