diff --git a/backend/patcher/lora.py b/backend/patcher/lora.py
index 8880b318..642109f4 100644
--- a/backend/patcher/lora.py
+++ b/backend/patcher/lora.py
@@ -127,6 +127,7 @@ def merge_lora_to_model_weight(patches, weight, key):
                     weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
             except Exception as e:
                 print("ERROR {} {} {}".format(patch_type, key, e))
+                raise e
         elif patch_type == "lokr":
             w1 = v[0]
             w2 = v[1]
@@ -173,6 +174,7 @@ def merge_lora_to_model_weight(patches, weight, key):
                     weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
             except Exception as e:
                 print("ERROR {} {} {}".format(patch_type, key, e))
+                raise e
         elif patch_type == "loha":
             w1a = v[0]
             w1b = v[1]
@@ -210,6 +212,7 @@ def merge_lora_to_model_weight(patches, weight, key):
                     weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
             except Exception as e:
                 print("ERROR {} {} {}".format(patch_type, key, e))
+                raise e
         elif patch_type == "glora":
             if v[4] is not None:
                 alpha = v[4] / v[0].shape[0]
@@ -231,6 +234,7 @@ def merge_lora_to_model_weight(patches, weight, key):
                     weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
             except Exception as e:
                 print("ERROR {} {} {}".format(patch_type, key, e))
+                raise e
         elif patch_type in extra_weight_calculators:
             weight = extra_weight_calculators[patch_type](weight, strength, v)
         else:
@@ -336,7 +340,12 @@ class LoraLoader:
                         weight = weight.data
 
             if target_device is not None:
-                weight = weight.to(device=target_device)
+                try:
+                    weight = weight.to(device=target_device)
+                except:
+                    print('Moving layer weight failed. Retrying by offload models.')
+                    self.model.to(device=offload_device)
+                    weight = weight.to(device=target_device)
 
             gguf_cls, gguf_type, gguf_real_shape = None, None, None
 
@@ -348,8 +357,15 @@ class LoraLoader:
                 weight = dequantize_tensor(weight)
 
             weight_original_dtype = weight.dtype
-            weight = weight.to(dtype=torch.float32)
-            weight = merge_lora_to_model_weight(current_patches, weight, key).to(dtype=weight_original_dtype)
+
+            try:
+                weight = weight.to(dtype=torch.float32)
+                weight = merge_lora_to_model_weight(current_patches, weight, key).to(dtype=weight_original_dtype)
+            except:
+                print('Patching LoRA weights failed. Retrying by offload models.')
+                self.model.to(device=offload_device)
+                weight = weight.to(dtype=torch.float32)
+                weight = merge_lora_to_model_weight(current_patches, weight, key).to(dtype=weight_original_dtype)
 
             if bnb_layer is not None:
                 bnb_layer.reload_weight(weight)