diff --git a/tutorials/v1/device/08_initialize_nnx_on_device.py b/tutorials/v1/device/08_initialize_nnx_on_device.py
index 3a4b76aa..8fdd26fc 100644
--- a/tutorials/v1/device/08_initialize_nnx_on_device.py
+++ b/tutorials/v1/device/08_initialize_nnx_on_device.py
@@ -84,7 +84,7 @@ def init_model_logic(input_dim: int, hidden_dim: int, output_dim: int, seed: int
     )
 
     # Split and convert state to pure dict
-    _graphdef, state = nnx.split(model)
+    _, state = nnx.split(model)
     state_dict = state.to_pure_dict()
 
     print(f"[Device P0] Initialized model with {len(state_dict)} parameter groups")
@@ -180,7 +180,7 @@ def init_model_with_optimizer_logic(
     )
 
     # Split and convert model state to pure dict
-    _graphdef, state = nnx.split(model)
+    _, state = nnx.split(model)
     model_state_dict = state.to_pure_dict()
 
     # Initialize optimizer and convert its state to pure dict
diff --git a/tutorials/v1/device/09_split_learning_vertical.py b/tutorials/v1/device/09_split_learning_vertical.py
index 68d141ea..e41252c8 100644
--- a/tutorials/v1/device/09_split_learning_vertical.py
+++ b/tutorials/v1/device/09_split_learning_vertical.py
@@ -646,14 +646,14 @@ def initialize_and_train_split_learning(
 
     def _init_alice_base():
         model = AliceBaseModel(input_dim=m1, hidden_dim=h1, rngs=nnx.Rngs(seed_alice))
-        _graphdef, state = nnx.split(model)
+        _, state = nnx.split(model)
         tx = optax.sgd(learning_rate)
         opt_state = tx.init(state.to_pure_dict())
         return model_state_to_dict(state, opt_state, 0)
 
     def _init_bob_base():
         model = BobBaseModel(input_dim=m2, hidden_dim=h2, rngs=nnx.Rngs(seed_bob))
-        _graphdef, state = nnx.split(model)
+        _, state = nnx.split(model)
         tx = optax.sgd(learning_rate)
         opt_state = tx.init(state.to_pure_dict())
         return model_state_to_dict(state, opt_state, 0)
@@ -665,7 +665,7 @@ def _init_alice_agg():
             output_dim=n_classes,
             rngs=nnx.Rngs(seed_agg),
         )
-        _graphdef, state = nnx.split(model)
+        _, state = nnx.split(model)
         tx = optax.sgd(learning_rate)
         opt_state = tx.init(state.to_pure_dict())
         return model_state_to_dict(state, opt_state, 0)