diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py
index 439a78de1..61a945bd3 100755
--- a/convert_lora_to_gguf.py
+++ b/convert_lora_to_gguf.py
@@ -338,28 +338,39 @@ if __name__ == '__main__':
             def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
                 tensor_map: dict[str, PartialLoraTensor] = {}
 
-                for name, tensor in lora_model.items():
-                    if self.lazy:
-                        tensor = LazyTorchTensor.from_eager(tensor)
-                    base_name = get_base_tensor_name(name)
-                    is_lora_a = ".lora_A.weight" in name
-                    is_lora_b = ".lora_B.weight" in name
-                    if not is_lora_a and not is_lora_b:
-                        if ".base_layer.weight" in name:
-                            continue
-                        logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor")
-                        sys.exit(1)
+                # The following edits will enable conversion for: SFTTrainer checkpoint adapter models and other adapter models that contain weights besides LoRA weights
 
-                    if base_name in tensor_map:
-                        if is_lora_a:
-                            tensor_map[base_name].A = tensor
+                #  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 self.lazy:
+                            tensor = LazyTorchTensor.from_eager(tensor)
+                        base_name = get_base_tensor_name(name)
+                        is_lora_a = ".lora_A.weight" in name
+                        is_lora_b = ".lora_B.weight" in name
+                        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
+                            else:
+                                tensor_map[base_name].B = tensor
                         else:
-                            tensor_map[base_name].B = tensor
-                    else:
-                        if is_lora_a:
-                            tensor_map[base_name] = PartialLoraTensor(A=tensor)
-                        else:
-                            tensor_map[base_name] = PartialLoraTensor(B=tensor)
+                            if is_lora_a:
+                                tensor_map[base_name] = PartialLoraTensor(A=tensor)
+                            else:
+                                tensor_map[base_name] = PartialLoraTensor(B=tensor)
 
                 for name, tensor in tensor_map.items():
                     assert tensor.A is not None