diff --git a/steer/models/model_wrapper.py b/steer/models/model_wrapper.py
index 0518bb94..e4bb6e85 100644
--- a/steer/models/model_wrapper.py
+++ b/steer/models/model_wrapper.py
@@ -461,6 +461,43 @@ def _decoder_layers(self):
         For text models this is exactly self.model.model.layers — identical to before."""
         return self._base_model().layers
 
+    @staticmethod
+    def _copy_activation_value(value):
+        if isinstance(value, t.Tensor):
+            return value.clone().detach()
+        try:
+            return copy.deepcopy(value)
+        except Exception:
+            return value
+
+    def _save_and_clear_generation_state(self, model_layers):
+        saved_layer_state = {}
+
+        for i, layer in enumerate(model_layers):
+            layer_state = {}
+            if hasattr(layer, 'add_activations_dict') and layer.add_activations_dict:
+                layer_state['add_activations_dict'] = {
+                    key: self._copy_activation_value(value)
+                    for key, value in layer.add_activations_dict.items()
+                }
+                layer.add_activations_dict = {}
+
+            if hasattr(layer, 'intervention_dict') and layer.intervention_dict:
+                layer_state['intervention_dict'] = dict(layer.intervention_dict)
+                layer.intervention_dict = {}
+
+            if layer_state:
+                saved_layer_state[i] = layer_state
+
+        return saved_layer_state
+
+    def _restore_generation_state(self, model_layers, saved_layer_state):
+        for i, layer_state in saved_layer_state.items():
+            if 'add_activations_dict' in layer_state:
+                model_layers[i].add_activations_dict = layer_state['add_activations_dict']
+            if 'intervention_dict' in layer_state:
+                model_layers[i].intervention_dict = layer_state['intervention_dict']
+
     def _lm_head(self):
         """The output projection. Override if a subclass nests it elsewhere."""
         return self.model.lm_head
@@ -587,24 +624,8 @@ def get_logits(self, tokens):
         return logits
     
     def ori_generate(self, input_ids, **kwargs):
-        # Save activation dictionaries
-        saved_activations = {}
         model_layers = self._decoder_layers()
-
-        for i, layer in enumerate(model_layers):
-            if hasattr(layer, 'add_activations_dict') and layer.add_activations_dict:
-                saved_dict = {}
-                for key, value in layer.add_activations_dict.items():
-                    if isinstance(value, t.Tensor):
-                        saved_dict[key] = value.clone().detach()
-                    else:
-                        try:
-                            saved_dict[key] = copy.deepcopy(value)
-                        except:
-                            saved_dict[key] = value
-                
-                saved_activations[i] = saved_dict
-                layer.add_activations_dict = {}
+        saved_layer_state = self._save_and_clear_generation_state(model_layers)
         
         # Save steer value if exists
         saved_steer_values = t.zeros(1)
@@ -619,9 +640,7 @@ def ori_generate(self, input_ids, **kwargs):
                 **kwargs
             )
         finally:
-            # Restore activation dictionaries
-            for i, activations_dict in saved_activations.items():
-                model_layers[i].add_activations_dict = activations_dict
+            self._restore_generation_state(model_layers, saved_layer_state)
             
             # Restore steer value
             if saved_steer_values is not None and hasattr(self, 'steer'):
@@ -630,24 +649,8 @@ def ori_generate(self, input_ids, **kwargs):
         return output
     
     def ori_vllm_generate(self, input_batch, vllm_sampling_params):
-        # Save activation dictionaries
-        saved_activations = {}
         model_layers = self._decoder_layers()
-
-        for i, layer in enumerate(model_layers):
-            if hasattr(layer, 'add_activations_dict') and layer.add_activations_dict:
-                saved_dict = {}
-                for key, value in layer.add_activations_dict.items():
-                    if isinstance(value, t.Tensor):
-                        saved_dict[key] = value.clone().detach()
-                    else:
-                        try:
-                            saved_dict[key] = copy.deepcopy(value)
-                        except:
-                            saved_dict[key] = value
-                
-                saved_activations[i] = saved_dict
-                layer.add_activations_dict = {}
+        saved_layer_state = self._save_and_clear_generation_state(model_layers)
         
         # Save steer value if exists
         saved_steer_values = t.zeros(1)
@@ -663,9 +666,7 @@ def ori_vllm_generate(self, input_batch, vllm_sampling_params):
             )
             
         finally:
-            # Restore activation dictionaries
-            for i, activations_dict in saved_activations.items():
-                model_layers[i].add_activations_dict = activations_dict
+            self._restore_generation_state(model_layers, saved_layer_state)
             
             # Restore steer value
             if saved_steer_values is not None and hasattr(self, 'steer'):
@@ -877,17 +878,8 @@ def reset(self, method_name):
             raise ValueError(f"Method {method_name} not supported to reset")
     
     def ori_generate(self, input_ids, **kwargs):
-        # Save activation dictionaries
-        saved_activations = {}
-        if hasattr(self.model, 'transformer') and isinstance(self.model.transformer, Hack_no_grad):
-            model_layers = self.model.transformer.module.h
-        else:
-            model_layers = self.model.transformer.h
-            
-        for i, layer in enumerate(model_layers):
-            if hasattr(layer, 'add_activations_dict') and layer.add_activations_dict:
-                saved_activations[i] = copy.deepcopy(layer.add_activations_dict)
-                layer.add_activations_dict = {}
+        model_layers = self._decoder_layers()
+        saved_layer_state = self._save_and_clear_generation_state(model_layers)
         
         # Save steer value if exists
         saved_steer_value = 0
@@ -902,9 +894,7 @@ def ori_generate(self, input_ids, **kwargs):
                 **kwargs
             )
         finally:
-            # Restore activation dictionaries
-            for i, activations_dict in saved_activations.items():
-                model_layers[i].add_activations_dict = activations_dict
+            self._restore_generation_state(model_layers, saved_layer_state)
             
             # Restore steer value
             if saved_steer_value is not None and hasattr(self, 'steer'):