diff --git a/pytorch_lightning/root_module/model_saving.py b/pytorch_lightning/root_module/model_saving.py
index 2537785175..ec55d5f62f 100644
--- a/pytorch_lightning/root_module/model_saving.py
+++ b/pytorch_lightning/root_module/model_saving.py
@@ -9,17 +9,19 @@ class ModelIO(object):
     def load_model_specific(self, checkpoint):
         """
         Do something with the checkpoint
+        Gives model a chance to load something before state_dict is restored
         :param checkpoint:
         :return:
         """
-        raise NotImplementedError
+        pass
 
     def get_save_dict(self):
         """
         Return specific things for the model
+        Called before trainer requests the state_dict
         :return:
         """
-        raise NotImplementedError
+        pass
 
     # -------------------------
     # OPTIONAL HOOKS
@@ -80,6 +82,7 @@ class TrainerIO(object):
         checkpoint_dict = model.get_save_dict()
 
         # merge trainer and model saving items
+        checkpoint['state_dict'] = checkpoint_dict
         checkpoint.update(checkpoint_dict)
         return checkpoint
 
@@ -167,13 +170,18 @@ class TrainerIO(object):
         else:
             checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)
 
-        # load training state
+        # load training state (affects trainer only)
         self.restore_training_state(checkpoint)
 
         # load model state
         model = self.__get_model()
+
+        # give model a chance to load something
         model.load_model_specific(checkpoint)
 
+        # load the state_dict on the model automatically
+        model.load_state_dict(checkpoint['state_dict'])
+
         # call model hook
         model.on_hpc_load()
 
diff --git a/pytorch_lightning/root_module/root_module.py b/pytorch_lightning/root_module/root_module.py
index d6d740f039..a25f7f6f85 100644
--- a/pytorch_lightning/root_module/root_module.py
+++ b/pytorch_lightning/root_module/root_module.py
@@ -110,9 +110,12 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
 
         model = cls(hparams)
 
-        # allow model to load
+        # give model a chance to load something
         model.load_model_specific(checkpoint)
-        model.load_state_dict(checkpoint['state_dict'], strict=False)
+
+        # load the state_dict on the model automatically
+        model.load_state_dict(checkpoint['state_dict'])
+
         return model
 
     def summarize(self):