diff --git a/src/sparseml/transformers/utils/trainer.py b/src/sparseml/transformers/utils/trainer.py
index 5df07d6e512..b824e6821df 100644
--- a/src/sparseml/transformers/utils/trainer.py
+++ b/src/sparseml/transformers/utils/trainer.py
@@ -137,46 +137,51 @@ def apply_recipes(self, epoch=0.0):
         Applies all recipes from checkpoint_recipes. Runs architecture changing
         modifiers to prepare model for state dict loading
         """
+        # get state dict before recipe application
+        org_state_dict = self.model.state_dict()
+
+        # apply any checkpoint recipes
         for checkpoint_recipe in self.checkpoint_recipes:
             if checkpoint_recipe is not None:
                 ScheduledModifierManager.from_yaml(checkpoint_recipe).apply(self.model)
+
+        # init current training recipe
         if self.manager is not None:
-            org_state_dict = self.model.state_dict()
             self.manager.initialize(
                 self.model,
                 epoch=epoch,
                 distillation_teacher=self.teacher,
                 loggers=self.loggers,
             )
-            new_state_dict = self.model.state_dict()
-            new_params = [p for p in new_state_dict.keys() if p not in org_state_dict]
-
-            if os.path.isdir(self.model_name_or_path):
-                if os.path.isfile(os.path.join(self.model_name_or_path, WEIGHTS_NAME)):
-                    archive_file = os.path.join(self.model_name_or_path, WEIGHTS_NAME)
-                    state_dict = torch.load(archive_file, map_location="cpu")
-                    new_params_to_init = [
-                        p for p in new_params if p in state_dict.keys()
-                    ]
-                    if new_params_to_init:
-                        # parameters from dict are dependent on recipe
-                        (
-                            _,
-                            missing_keys,
-                            unexpected_keys,
-                            _,
-                        ) = self.model._load_state_dict_into_model(
-                            self.model,
-                            state_dict,
-                            self.model_name_or_path,
-                            _fast_init=False,
+
+        # if model structure changed, load in new params from state dict
+        new_state_dict = self.model.state_dict()
+        new_params = [p for p in new_state_dict.keys() if p not in org_state_dict]
+
+        if os.path.isdir(self.model_name_or_path):
+            if os.path.isfile(os.path.join(self.model_name_or_path, WEIGHTS_NAME)):
+                archive_file = os.path.join(self.model_name_or_path, WEIGHTS_NAME)
+                state_dict = torch.load(archive_file, map_location="cpu")
+                new_params_to_init = [p for p in new_params if p in state_dict.keys()]
+                if new_params_to_init:
+                    # parameters from dict are dependent on recipe
+                    (
+                        _,
+                        missing_keys,
+                        unexpected_keys,
+                        _,
+                    ) = self.model._load_state_dict_into_model(
+                        self.model,
+                        state_dict,
+                        self.model_name_or_path,
+                        _fast_init=False,
+                    )
+                    if missing_keys or unexpected_keys:
+                        raise RuntimeError(
+                            "Unexpected or missing keys detected when applying "
+                            f"recipes to models\nMissing keys: {missing_keys}\n"
+                            f"Unexpected keys: {unexpected_keys}\n"
                         )
-                        if missing_keys or unexpected_keys:
-                            raise RuntimeError(
-                                "Unexpected or missing keys detected when applying "
-                                f"recipes to models\nMissing keys: {missing_keys}\n"
-                                f"Unexpected keys: {unexpected_keys}\n"
-                            )
 
     def create_optimizer(self):
         """