diff --git a/docs/user_guide/advanced_usage.rst b/docs/user_guide/advanced_usage.rst
index 091fcb989a8b16b05de058bd8d80e1038c177a8a..0f9d76093077835b71623edd274801f277a07ae8 100644
--- a/docs/user_guide/advanced_usage.rst
+++ b/docs/user_guide/advanced_usage.rst
@@ -577,12 +577,16 @@ so MACE provides several ways to reduce the model size with no or little perform
 
 **1. Save model weights in half-precision floating point format**
 
-The default data type of a regular model is float (32bit). To reduce the model weights size,
+The data type of a regular model is float (32bit). To reduce the model weights size,
 half (16bit) can be used to reduce it by half with negligible accuracy degradation.
+Therefore, the default storage type for a regular model in MACE is half. However,
+if the model is very sensitive to accuracy, storage type can be changed to float.
 
-For CPU, ``data_type`` can be specified as ``fp16_fp32`` in the deployment file to save the weights in half and actual inference in float.
+In the deployment file, ``data_type`` is ``fp16_fp32`` by default and can be changed to ``fp32_fp32``.
 
-For GPU, ``fp16_fp32`` is default. The ops in GPU take half as inputs and outputs while kernel execution in float.
+For CPU, ``fp16_fp32`` means that the weights are saved in half and actual inference is in float.
+
+For GPU, ``fp16_fp32`` means that the ops in GPU take half as inputs and outputs while kernel execution in float.
 
 **2. Save model weights in quantized fixed point format**
 
diff --git a/docs/user_guide/advanced_usage_cmake.rst b/docs/user_guide/advanced_usage_cmake.rst
index 87d17fe4133c89724fd51c563bc9a1cf47f42dfe..7be5e2f227a6950ae83bc7bb9d218cd1fcb1a87d 100644
--- a/docs/user_guide/advanced_usage_cmake.rst
+++ b/docs/user_guide/advanced_usage_cmake.rst
@@ -406,12 +406,16 @@ so MACE provides several ways to reduce the model size with no or little perform
 
 **1. Save model weights in half-precision floating point format**
 
-The default data type of a regular model is float (32bit). To reduce the model weights size,
+The data type of a regular model is float (32bit). To reduce the model weights size,
 half (16bit) can be used to reduce it by half with negligible accuracy degradation.
+Therefore, the default storage type for a regular model in MACE is half. However,
+if the model is very sensitive to accuracy, storage type can be changed to float.
 
-For CPU, ``data_type`` can be specified as ``fp16_fp32`` in the deployment file to save the weights in half and actual inference in float.
+In the deployment file, ``data_type`` is ``fp16_fp32`` by default and can be changed to ``fp32_fp32``.
 
-For GPU, ``fp16_fp32`` is default. The ops in GPU take half as inputs and outputs while kernel execution in float.
+For CPU, ``fp16_fp32`` means that the weights are saved in half and actual inference is in float.
+
+For GPU, ``fp16_fp32`` means that the ops in GPU take half as inputs and outputs while kernel execution in float.
 
 **2. Save model weights in quantized fixed point format**
 
diff --git a/tools/python/utils/config_parser.py b/tools/python/utils/config_parser.py
index 9e5c9f6dc843e8bb9a54f1e65fc99fd75ab1b455..5a56fd3c4e54ce27920939579522da7f673afdad 100644
--- a/tools/python/utils/config_parser.py
+++ b/tools/python/utils/config_parser.py
@@ -204,7 +204,7 @@ def normalize_model_config(conf):
     conf[ModelKeys.platform] = parse_platform(conf[ModelKeys.platform])
     conf[ModelKeys.runtime] = parse_device_type(conf[ModelKeys.runtime])
 
-    if ModelKeys.quantize in conf:
+    if ModelKeys.quantize in conf and conf[ModelKeys.quantize] == 1:
         conf[ModelKeys.data_type] = mace_pb2.DT_FLOAT
     else:
         if ModelKeys.data_type in conf: