diff --git a/paddle_hub/module.py b/paddle_hub/module.py
index 40d196382c8b21264c431dfeb684dd4aeeaa68c2..233310263364149ace24ae601f4ff21ee96b17ee 100644
--- a/paddle_hub/module.py
+++ b/paddle_hub/module.py
@@ -74,33 +74,6 @@ class Module(object):
             self.module_name = module_dir.split("/")[-1]
             #TODO(ZeyuChen) add more check about loading module from local path
 
-        # load paddle inference model
-        place = fluid.CPUPlace()
-        model_dir = os.path.join(self.module_dir, MODEL_DIRNAME)
-        self.exe = fluid.Executor(fluid.CPUPlace())
-        [self.inference_program, self.feed_target_names,
-         self.fetch_targets] = fluid.io.load_inference_model(
-             dirname=model_dir, executor=self.exe)
-
-        # remove feed fetch operator and variable
-        ModuleUtils.remove_feed_fetch_op(self.inference_program)
-
-        # print("inference_program")
-        # print(self.inference_program)
-        print("**feed_target_names**\n{}".format(self.feed_target_names))
-        print("**fetch_targets**\n{}".format(self.fetch_targets))
-
-        self.config = ModuleConfig(self.module_dir)
-        self.config.load()
-        self._process_parameter()
-        #TODO(wuzewu): recover the default unique name generator someother where
-        self._process_uqn()
-
-    def _process_uqn(self):
-        name_generator_path = ModuleConfig.name_generator_path(self.module_dir)
-        with open(name_generator_path, "rb") as fi:
-            fluid.unique_name.switch(pickle.load(fi))
-
     def _process_parameter(self):
         global_block = self.inference_program.global_block()
         param_path = ModuleConfig.meta_param_path(self.module_dir)
@@ -133,12 +106,33 @@ class Module(object):
                 if op.has_attr("is_test"):
                     op._set_attr("is_test", is_test)
 
+        # load paddle inference model
+        place = fluid.CPUPlace()
+        model_dir = os.path.join(self.module_dir, MODEL_DIRNAME)
+        self.exe = fluid.Executor(fluid.CPUPlace())
+        self.inference_program, self.feed_target_names, self.fetch_targets = fluid.io.load_inference_model(
+            dirname=os.path.join(model_dir, sign_name, executor=self.exe))
+
+        # remove feed fetch operator and variable
+        ModuleUtils.remove_feed_fetch_op(self.inference_program)
+        # print("inference_program")
+        # print(self.inference_program)
+        print("**feed_target_names**\n{}".format(self.feed_target_names))
+        print("**fetch_targets**\n{}".format(self.fetch_targets))
+
+        self.config = ModuleConfig(self.module_dir)
+        self.config.load()
+        self._process_parameter()
+        name_generator_path = ModuleConfig.name_generator_path(self.module_dir)
+        with open(name_generator_path, "rb") as data:
+            generator = pickle.load(data)
+
         program = self.get_inference_program().clone()
 
         _process_op_attr(program=program, is_test=False)
         _set_param_trainable(program=program, trainable=trainable)
 
-        return self.feed_target_names, self.fetch_targets, program
+        return self.feed_target_names, self.fetch_targets, program, generator
 
     def get_inference_program(self):
         return self.inference_program
@@ -323,14 +317,15 @@ def create_module(sign_arr, program, module_dir=None, word_dict=None):
     exe = fluid.Executor(place=fluid.CPUPlace())
     model_dir = os.path.join(module_dir, "model")
     mkdir(model_dir)
-    # TODO(ZeyuChen): here only deal with one signature
-    first_sign = sign_arr[0]
-    fluid.io.save_inference_model(
-        model_dir,
-        feeded_var_names=[var.name for var in first_sign.get_inputs()],
-        target_vars=first_sign.get_outputs(),
-        main_program=program,
-        executor=exe)
+    # TODO(wuzewu): save paddle model with a more effective way
+    for sign in sign_arr:
+        save_model_dir = os.path.join(model_dir, sign.get_name())
+        fluid.io.save_inference_model(
+            save_model_dir,
+            feeded_var_names=[var.name for var in sign.get_inputs()],
+            target_vars=sign.get_outputs(),
+            main_program=program,
+            executor=exe)
 
     # save to disk
     data = module.SerializeToString()