diff --git a/custom_linear.py b/custom_linear.py
index ff3e7fc..7234c4c 100644
--- a/custom_linear.py
+++ b/custom_linear.py
@@ -101,7 +101,7 @@ class CustomLinear(nn.Linear):
 
         return torch.nn.functional.linear(input, weight, bias)
 
-    @torch.compiler.disable()
+    #@torch.compiler.disable()
     def apply_lora(self, weight):
         for lora_diff, lora_strength in zip(self.lora[0], self.lora[1]):
             if isinstance(lora_strength, list):
@@ -113,7 +113,7 @@ class CustomLinear(nn.Linear):
             patch_diff = torch.mm(
                 lora_diff[0].flatten(start_dim=1).to(weight.device),
                 lora_diff[1].flatten(start_dim=1).to(weight.device)
-            ).reshape(weight.shape)
+            ).reshape(weight.shape) + 0
             alpha = lora_diff[2] / lora_diff[1].shape[0] if lora_diff[2] is not None else 1.0
             scale = lora_strength * alpha
             weight = weight.add(patch_diff, alpha=scale)