diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py
index 61a945bd3..26de8b1ca 100755
--- a/convert_lora_to_gguf.py
+++ b/convert_lora_to_gguf.py
@@ -338,16 +338,8 @@ if __name__ == '__main__':
             def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
                 tensor_map: dict[str, PartialLoraTensor] = {}
 
-                # The following edits will enable conversion for: SFTTrainer checkpoint adapter models and other adapter models that contain weights besides LoRA weights
-
-                #  Here, we first get the items with the 'lora_' substring
-                lora_model_items_name = [name for name,_ in lora_model.items()]
-                lora_model_items_with_lora_tensors = [name for name in lora_model_items_name if 'lora_' in name]
-
                 for name, tensor in lora_model.items():
-
-                    # Check for only LoRA finetuned weights and base layer weights
-                    if (name in lora_model_items_with_lora_tensors) or (".base_layer.weight" in name):
+                    if ("lora_" in name) or (".base_layer.weight" in name):
                         if self.lazy:
                             tensor = LazyTorchTensor.from_eager(tensor)
                         base_name = get_base_tensor_name(name)
@@ -356,11 +348,7 @@ if __name__ == '__main__':
                         if not is_lora_a and not is_lora_b:
                             if ".base_layer.weight" in name:
                                 continue
-
-                            # we will either have a lora weight or a base layer weight, this error becomes trivial
-                            # logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor")
-                            # sys.exit(1)
-
+                                
                         if base_name in tensor_map:
                             if is_lora_a:
                                 tensor_map[base_name].A = tensor
@@ -371,6 +359,8 @@ if __name__ == '__main__':
                                 tensor_map[base_name] = PartialLoraTensor(A=tensor)
                             else:
                                 tensor_map[base_name] = PartialLoraTensor(B=tensor)
+                    else:
+                        pass
 
                 for name, tensor in tensor_map.items():
                     assert tensor.A is not None