diff --git a/examples/pytorch/quantized_model_init/fully_shard.py b/examples/pytorch/quantized_model_init/fully_shard.py
index 2b5ca84ebc..1a8b716b22 100644
--- a/examples/pytorch/quantized_model_init/fully_shard.py
+++ b/examples/pytorch/quantized_model_init/fully_shard.py
@@ -43,6 +43,7 @@
 import transformer_engine.pytorch as te
 from transformer_engine.pytorch.module.base import TransformerEngineBaseModule
 from transformer_engine.pytorch.quantized_tensor import QuantizedTensor
+from transformer_engine.common.recipe import MXFP8BlockScaling
 
 # ── Configuration ────────────────────────────────────────────────────
 HIDDEN_SIZE = 256
@@ -85,7 +86,9 @@ def main():
     # avoiding the precision loss of dequantizing from FP8.
     # We set DTYPE to float32 since these weights will actually be initialized as FP8,
     # but we want to seed the optimizer states (which will be in FP32) with the FP32 values.
-    with te.quantized_model_init(enabled=True, preserve_high_precision_init_val=True):
+    with te.quantized_model_init(
+        recipe=MXFP8BlockScaling(), enabled=True, preserve_high_precision_init_val=True
+    ):
         model = torch.nn.Sequential(
             *[
                 te.TransformerLayer(
@@ -154,7 +157,7 @@ def main():
     for step in range(NUM_STEPS):
         optimizer.zero_grad(set_to_none=True)
 
-        with te.autocast(enabled=True):
+        with te.autocast(enabled=True, recipe=MXFP8BlockScaling()):
             output = model(x)
 
         loss = F.mse_loss(output, target)
@@ -187,7 +190,7 @@ def main():
 
     # Verify training continues after checkpoint load.
     optimizer.zero_grad(set_to_none=True)
-    with te.autocast(enabled=True):
+    with te.autocast(enabled=True, recipe=MXFP8BlockScaling()):
         output = model(x)
     loss = F.mse_loss(output, target)
     loss.backward()