diff --git a/mindspore/nn/layer/quant.py b/mindspore/nn/layer/quant.py
index 412c659b2a..6cdcea0a38 100644
--- a/mindspore/nn/layer/quant.py
+++ b/mindspore/nn/layer/quant.py
@@ -1446,7 +1446,6 @@ class QuantMindirBlock(Cell):
         if isinstance(activation, ReLU):
             self.activation = None
             self.has_act = False
-        self.bias_add = P.BiasAdd()
 
     def construct(self, x):
         if self.has_bias:
diff --git a/mindspore/train/quant/quant.py b/mindspore/train/quant/quant.py
index faa7cbed76..9d64704d81 100644
--- a/mindspore/train/quant/quant.py
+++ b/mindspore/train/quant/quant.py
@@ -361,12 +361,12 @@ class ExportToQuantInferNetwork:
         param_dict["symmetric"] = fake_quant_a_out.symmetric
         if self.is_mindir:
             scale_w, zp_w, param_dict["filter_maxq"], param_dict["filter_minq"] = \
-                quant_utils.scale_zp_max_min_from_fack_quant_cell(cell_core.fake_quant_weight, np_type)
+                quant_utils.scale_zp_max_min_from_fake_quant_cell(cell_core.fake_quant_weight, np_type)
             scale_a_out, _, param_dict["output_maxq"], param_dict["output_minq"] = \
-                quant_utils.scale_zp_max_min_from_fack_quant_cell(fake_quant_a_out, np_type)
+                quant_utils.scale_zp_max_min_from_fake_quant_cell(fake_quant_a_out, np_type)
         else:
-            scale_w, zp_w = quant_utils.scale_zp_from_fack_quant_cell(cell_core.fake_quant_weight, np_type)
-            scale_a_out, _ = quant_utils.scale_zp_from_fack_quant_cell(fake_quant_a_out, np_type)
+            scale_w, zp_w = quant_utils.scale_zp_from_fake_quant_cell(cell_core.fake_quant_weight, np_type)
+            scale_a_out, _ = quant_utils.scale_zp_from_fake_quant_cell(fake_quant_a_out, np_type)
         info = self.quant_info_table.get(w_minq_name, None)
         if info:
             fack_quant_a_in_op, minq_name = info
@@ -432,7 +432,8 @@ class ExportToQuantInferNetwork:
             op_core = cell_core.conv
         weight = Tensor(weight, self.data_type)
         weight_b = Tensor(weight_b)
-        bias_b = Tensor(bias_b, mstype.float32)
+        if bias_b is not None:
+            bias_b = Tensor(bias_b, mstype.float32)
         if self.is_mindir:
             block = quant.QuantMindirBlock(op_core, weight_b, bias_b, activation, param_dict)
         else:
diff --git a/mindspore/train/quant/quant_utils.py b/mindspore/train/quant/quant_utils.py
index d04a63faea..70a4093e44 100644
--- a/mindspore/train/quant/quant_utils.py
+++ b/mindspore/train/quant/quant_utils.py
@@ -122,7 +122,7 @@ def weight2int(data, scale, zero_point):
     return np.round((data / scale) + zero_point)
 
 
-def scale_zp_from_fack_quant_cell(cell, data_type):
+def scale_zp_from_fake_quant_cell(cell, data_type):
     r"""
     Get calculate quantization params for scale and zero point From `FakeQuantWithMinMax`.
 
@@ -146,7 +146,7 @@ def scale_zp_from_fack_quant_cell(cell, data_type):
     return scale, zp
 
 
-def scale_zp_max_min_from_fack_quant_cell(cell, data_type):
+def scale_zp_max_min_from_fake_quant_cell(cell, data_type):
     """Get calculate quantization params for scale, zero point, max and min from `FakeQuantWithMinMax`."""
     minq = cell.minq.data.asnumpy()
     maxq = cell.maxq.data.asnumpy()
diff --git a/mindspore/train/serialization.py b/mindspore/train/serialization.py
index b615d48ea6..d9b620d05b 100644
--- a/mindspore/train/serialization.py
+++ b/mindspore/train/serialization.py
@@ -175,10 +175,7 @@ def save_checkpoint(save_obj, ckpt_file_name, integrated_save=True, async_save=F
         param_list = []
         for (key, value) in param_dict.items():
             each_param = {"name": key}
-            if isinstance(value.data, Tensor):
-                param_data = value.data
-            else:
-                param_data = Tensor(value.data)
+            param_data = Tensor(value.data)
 
             # in automatic model parallel scenario, some parameters were spliteds to all the devices,
             # which should be combined before saving
diff --git a/model_zoo/official/cv/mobilenetv2_quant/export.py b/model_zoo/official/cv/mobilenetv2_quant/export.py
index f9fe15e4cf..6f4ad6aac1 100644
--- a/model_zoo/official/cv/mobilenetv2_quant/export.py
+++ b/model_zoo/official/cv/mobilenetv2_quant/export.py
@@ -24,7 +24,7 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.train.quant import quant
 
 from src.mobilenetV2 import mobilenetV2
-from src.config import config_ascend
+from src.config import config_ascend_quant
 
 parser = argparse.ArgumentParser(description='Image classification')
 parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
@@ -34,7 +34,7 @@ args_opt = parser.parse_args()
 if __name__ == '__main__':
     cfg = None
     if args_opt.device_target == "Ascend":
-        cfg = config_ascend
+        cfg = config_ascend_quant
         context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
     else:
         raise ValueError("Unsupported device target: {}.".format(args_opt.device_target))
@@ -50,5 +50,5 @@ if __name__ == '__main__':
     # export network
     print("============== Starting export ==============")
     inputs = Tensor(np.ones([1, 3, cfg.image_height, cfg.image_width]), mindspore.float32)
-    quant.export(network, inputs, file_name="mobilenet_quant", file_format='AIR')
+    quant.export(network, inputs, file_name="mobilenet_quant", file_format='MINDIR')
     print("============== End export ==============")