diff --git a/neural_compressor/adaptor/onnxrt.py b/neural_compressor/adaptor/onnxrt.py
index 710d235e171..c23d702aa60 100644
--- a/neural_compressor/adaptor/onnxrt.py
+++ b/neural_compressor/adaptor/onnxrt.py
@@ -245,10 +245,12 @@ def quantize(self, tune_cfg, model, data_loader, q_func=None):
         if model.model.opset_import[0].version < 11: # pragma: no cover
             logger.warning("Quantize input needs model opset 11 or newer.")
         if self.backend == 'DnnlExecutionProvider' and \
-            any([i.domain in ['', 'ai.onnx'] and i.version < 15 for i in model.model.opset_import]):
+            any([i.domain in ['', 'ai.onnx'] and \
+            i.version < 15 for i in model.model.opset_import]): # pragma: no cover
             from onnx import version_converter
+            from neural_compressor.model.onnx_model import ONNXModel
             try:
-                model.model = self._rename_node(version_converter.convert_version(model.model, 15))
+                model = self._rename_node(ONNXModel(version_converter.convert_version(model.model, 15)))
             except:
                 logging.warning("Fail to upgrade model opset_import to >= 15, "\
                                 "please upgrate it manually to run with bf16 data type")
@@ -716,7 +718,7 @@ def _pre_optimize(self, model, level=1):
         model.model = self._replace_gemm_with_matmul(tmp_model).model if \
             options.onnxrt.graph_optimization.gemm2matmul and self.recipes.get('gemm_to_matmul', True) else \
             tmp_model
-        model.model = self._rename_node(model.model)
+        model = self._rename_node(model)
         model = self._revert_fusedconv(model)
         if self.backend == 'TensorrtExecutionProvider':
             model = self._revert_conv_add_fusion(model)
@@ -788,7 +790,8 @@ def _revert_fusedconv(self, model):
         model.update()
         return model
 
-    def _rename_node(self, model):
+    def _rename_node(self, model_wrapper):
+        model = model_wrapper.model
         node_names = [i.name for i in model.graph.node]
         if len(set(node_names)) < len(node_names):
             logger.warning("This model has nodes with the same name, please check" \
@@ -797,8 +800,16 @@ def _rename_node(self, model):
             for idx, node in enumerate(model.graph.node):
                 if node_names.count(node.name) > 1:
                     node.name = node.op_type + '_nc_rename_' + str(idx)
-            onnx.save(model, os.path.join(self.work_space, "renamed_model.onnx")) 
-        return model
+            if model_wrapper.is_large_model:
+                onnx.save(model,
+                          os.path.join(self.work_space, "renamed_model.onnx"),
+                          save_as_external_data=True,
+                          all_tensors_to_one_file=True,
+                          location="weights.pb",
+                          convert_attribute=False)
+            else:
+                onnx.save(model, os.path.join(self.work_space, "renamed_model.onnx")) 
+        return model_wrapper
 
     @staticmethod
     def _replace_gemm_with_matmul(model):
diff --git a/neural_compressor/adaptor/ox_utils/quantizer.py b/neural_compressor/adaptor/ox_utils/quantizer.py
index c6328112724..cee4dda3def 100644
--- a/neural_compressor/adaptor/ox_utils/quantizer.py
+++ b/neural_compressor/adaptor/ox_utils/quantizer.py
@@ -336,6 +336,7 @@ def dfs(match_nodes, node, pattern):
                     children = self.model.get_children(match_nodes[1])
                     input_dtype = '1' # float32
                     output_dtype = '1' # 'float32'
+                    outs = None
                     for inp in parent.input:
                         if inp in self.new_value_info:
                             input_dtype = str(self.new_value_info[inp].new_dtype)
@@ -345,7 +346,7 @@ def dfs(match_nodes, node, pattern):
                         if len(outs) > 0:
                             output_dtype = str(self.new_value_info[outs[0]].new_dtype)
                             break
-                    if len(outs) == 0 or all([not self.should_cast(i) for i in children]):
+                    if outs is None or len(outs) == 0 or all([not self.should_cast(i) for i in children]):
                         return
                     if input_dtype == str(match_nodes[1].attribute[0].i) and \
                         output_dtype == str(match_nodes[0].attribute[0].i) and \
diff --git a/test/adaptor/onnxrt_adaptor/test_adaptor_onnxrt.py b/test/adaptor/onnxrt_adaptor/test_adaptor_onnxrt.py
index de70b703970..80d988a84f3 100644
--- a/test/adaptor/onnxrt_adaptor/test_adaptor_onnxrt.py
+++ b/test/adaptor/onnxrt_adaptor/test_adaptor_onnxrt.py
@@ -770,6 +770,7 @@ def tearDownClass(self):
         os.remove("benchmark.yaml")
         os.remove("gather.yaml")
         os.remove("rename.yaml")
+        os.remove("rename_model.onnx")
         os.remove("rn50_9.onnx")
         os.remove(self.mb_v2_export_path)
         os.remove(self.rn50_export_path)
@@ -1007,6 +1008,20 @@ def test_adaptor(self):
         q_model = quantizer.fit()
         self.assertNotEqual(q_model, None)
 
+        conf.model.framework = 'onnxrt_integerops'
+        conf.quantization.approach = 'post_training_dynamic_quant'
+        conf.quantization.calibration.sampling_size = 1
+        conf.evaluation.accuracy.metric = {'MSE': {'compare_label': False}}
+        quantizer = Quantization(conf)
+        quantizer.calib_dataloader = self.rename_dataloader
+        quantizer.eval_dataloader = self.rename_dataloader
+        onnx.save(self.rename_model, 'rename_model.onnx')
+        quantizer.model = 'rename_model.onnx'
+        # force set the model to large model
+        quantizer.model._is_large_model = True
+        q_model = quantizer.fit()
+        self.assertNotEqual(q_model, None)
+
         quantizer = Quantization("dynamic.yaml")
         quantizer.calib_dataloader = self.cv_dataloader
         quantizer.eval_dataloader = self.cv_dataloader