diff --git a/training/train.py b/training/train.py
index 70fd774..51de18d 100644
--- a/training/train.py
+++ b/training/train.py
@@ -99,7 +99,7 @@ def __init__(self, feat_type: str = "cls", model_type: str = "dino_vitb16", stri
         pytorch_total_params = sum(p.numel() for p in self.perceptual_model.parameters())
         pytorch_total_trainable_params = sum(p.numel() for p in self.perceptual_model.parameters() if p.requires_grad)
         print(f'Total params: {pytorch_total_params} | Trainable params: {pytorch_total_trainable_params} '
-              f'| % Trainable: {pytorch_total_trainable_params/pytorch_total_params}')
+              f'| % Trainable: {pytorch_total_trainable_params/pytorch_total_params * 100}')
 
         self.criterion = HingeLoss(margin=self.margin, device=device)
 
@@ -145,14 +145,6 @@ def on_train_epoch_end(self):
         if self.use_lora:
             self.__save_lora_weights()
 
-    def on_train_start(self):
-        for extractor in self.perceptual_model.extractor_list:
-            extractor.model.train()
-
-    def on_validation_start(self):
-        for extractor in self.perceptual_model.extractor_list:
-            extractor.model.eval()
-
     def on_validation_epoch_start(self):
         self.__reset_val_metrics()
 
@@ -163,7 +155,7 @@ def on_validation_epoch_end(self):
 
         self.log(f'val_acc_ckpt', score, logger=False)
         self.log(f'val_loss_ckpt', loss, logger=False)
-        # log for tensorboard
+
         self.logger.experiment.add_scalar(f'val_2afc_acc/', score, epoch)
         self.logger.experiment.add_scalar(f'val_loss/', loss, epoch)
 
@@ -190,17 +182,14 @@ def __reset_val_metrics(self):
             v.reset()
 
     def __prep_lora_model(self):
-        for extractor in self.perceptual_model.extractor_list:
-            config = LoraConfig(
-                r=self.lora_r,
-                lora_alpha=self.lora_alpha,
-                lora_dropout=self.lora_dropout,
-                bias='none',
-                target_modules=['qkv']
-            )
-            extractor_model = get_peft_model(ViTModel(extractor.model, ViTConfig()),
-                                             config).to(extractor.device)
-            extractor.model = extractor_model
+        config = LoraConfig(
+            r=self.lora_r,
+            lora_alpha=self.lora_alpha,
+            lora_dropout=self.lora_dropout,
+            bias='none',
+            target_modules=['qkv']
+        )
+        self.perceptual_model = get_peft_model(self.perceptual_model, config)
 
     def __prep_linear_model(self):
         for extractor in self.perceptual_model.extractor_list: