diff --git a/evaluation/eval_percep.py b/evaluation/eval_percep.py
index 53377a7..f49bb88 100644
--- a/evaluation/eval_percep.py
+++ b/evaluation/eval_percep.py
@@ -52,7 +52,7 @@ def load_dreamsim_model(args, device="cuda"):
     with open(os.path.join(args.eval_checkpoint_cfg), "r") as f:
         cfg = yaml.load(f, Loader=yaml.Loader)
 
-    model_cfg = vars(cfg)
+    model_cfg = cfg
     model_cfg['load_dir'] = args.load_dir
     model = LightningPerceptualModel(**model_cfg)
     model.load_lora_weights(args.eval_checkpoint)
@@ -141,4 +141,4 @@ def run(args, device):
     args = parse_args()
     device = "cuda" if torch.cuda.is_available() else "cpu"
     run(args, device)
-    
\ No newline at end of file
+    
diff --git a/training/train.py b/training/train.py
index f8105ef..e25aabb 100644
--- a/training/train.py
+++ b/training/train.py
@@ -184,7 +184,13 @@ def load_lora_weights(self, checkpoint_root, epoch_load=None):
         if self.save_mode in {'adapter_only', 'all'}:
             if epoch_load is not None:
                 checkpoint_root = os.path.join(checkpoint_root, f'epoch_{epoch_load}')
-
+                
+            with open(os.path.join(checkpoint_root, 'adapter_config.json'), 'r') as f:
+                adapter_config = json.load(f)
+            lora_keys = ['r', 'lora_alpha', 'lora_dropout', 'bias', 'target_modules']
+            lora_config = LoraConfig(**{k: adapter_config[k] for k in lora_keys})
+            self.perceptual_model = get_peft_model(self.perceptual_model, lora_config)
+            
             logging.info(f'Loading adapter weights from {checkpoint_root}')
             self.perceptual_model = PeftModel.from_pretrained(self.perceptual_model.base_model.model, checkpoint_root).to(self.device)
         else: