diff --git a/src/sagemaker_pytorch_serving_container/handler_service.py b/src/sagemaker_pytorch_serving_container/handler_service.py
index 07e81c7b..518dc231 100644
--- a/src/sagemaker_pytorch_serving_container/handler_service.py
+++ b/src/sagemaker_pytorch_serving_container/handler_service.py
@@ -13,7 +13,7 @@
 from __future__ import absolute_import
 
 from sagemaker_inference.default_handler_service import DefaultHandlerService
-from sagemaker_inference.transformer import Transformer
+from sagemaker_pytorch_serving_container.transformer import PyTorchTransformer
 from sagemaker_pytorch_serving_container.default_pytorch_inference_handler import DefaultPytorchInferenceHandler
 
 import os
@@ -38,7 +38,7 @@ class HandlerService(DefaultHandlerService):
     def __init__(self):
         self._initialized = False
 
-        transformer = Transformer(default_inference_handler=DefaultPytorchInferenceHandler())
+        transformer = PyTorchTransformer(default_inference_handler=DefaultPytorchInferenceHandler())
         super(HandlerService, self).__init__(transformer=transformer)
 
     def initialize(self, context):
diff --git a/src/sagemaker_pytorch_serving_container/torchserve.py b/src/sagemaker_pytorch_serving_container/torchserve.py
index 048a06b4..64211221 100644
--- a/src/sagemaker_pytorch_serving_container/torchserve.py
+++ b/src/sagemaker_pytorch_serving_container/torchserve.py
@@ -138,7 +138,7 @@ def _generate_ts_config_properties(handler_service):
     if ts_env.is_env_set() and not ENABLE_MULTI_MODEL:
         models_string = f'''{{\\
         "{DEFAULT_TS_MODEL_NAME}": {{\\
-            "1.0": {{\\
+            "1": {{\\
                 "defaultVersion": true,\\
                 "marName": "{DEFAULT_TS_MODEL_NAME}.mar",\\
                 "minWorkers": {ts_env._min_workers},\\
diff --git a/src/sagemaker_pytorch_serving_container/transformer.py b/src/sagemaker_pytorch_serving_container/transformer.py
new file mode 100644
index 00000000..5550e2a1
--- /dev/null
+++ b/src/sagemaker_pytorch_serving_container/transformer.py
@@ -0,0 +1,70 @@
+from six.moves import http_client
+
+from sagemaker_inference.transformer import Transformer
+from sagemaker_inference import content_types, environment, utils
+from sagemaker_inference.default_inference_handler import DefaultInferenceHandler
+from sagemaker_inference.errors import BaseInferenceToolkitError, GenericInferenceToolkitError
+
+
+class PyTorchTransformer(Transformer):
+    def transform(self, data, context):
+        """
+        Take a request with input data, deserialize it, make a prediction, and return a
+        serialized response.
+
+        Args:
+            data (obj): the request data.
+            context (obj): metadata on the incoming request data.
+
+        Returns:
+            list[obj]: The serialized prediction result wrapped in a list if
+                inference is successful. Otherwise returns an error message
+                with the context set appropriately.
+        """
+        try:
+            properties = context.system_properties
+            model_dir = properties.get("model_dir")
+            self.validate_and_initialize(model_dir=model_dir)
+
+            response_list = []
+
+            for _ in range(len(data)):
+                input_data = data[i].get("body")
+
+                request_processor = context.request_processor[0]
+
+                request_property = request_processor.get_request_properties()
+                content_type = utils.retrieve_content_type_header(request_property)
+                accept = request_property.get("Accept") or request_property.get("accept")
+
+                if not accept or accept == content_types.ANY:
+                    accept = self._environment.default_accept
+
+                if content_type in content_types.UTF8_TYPES:
+                    input_data = input_data.decode("utf-8")
+
+                result = self._transform_fn(self._model, input_data, content_type, accept)
+
+                response = result
+                response_content_type = accept
+
+                if isinstance(result, tuple):
+                    # handles tuple for backwards compatibility
+                    response = result[0]
+                    response_content_type = result[1]
+
+                context.set_response_content_type(0, response_content_type)
+
+                response_list.append(response)
+
+            return response_list
+        except Exception as e:  # pylint: disable=broad-except
+            trace = traceback.format_exc()
+            if isinstance(e, BaseInferenceToolkitError):
+                return self.handle_error(context, e, trace)
+            else:
+                return self.handle_error(
+                    context,
+                    GenericInferenceToolkitError(http_client.INTERNAL_SERVER_ERROR, str(e)),
+                    trace,
+                )