diff --git a/examples/open_clip/difference.py b/examples/open_clip/difference.py
index b2ca1a1b8..4117cea57 100644
--- a/examples/open_clip/difference.py
+++ b/examples/open_clip/difference.py
@@ -33,18 +33,8 @@ def parse_args(args):
 
 
 def difference(a_file, b_file):
-    file = open(a_file, "r")
-    a_variable = eval(file.read())
-    file.close()
-
-    file = open(b_file, "r")
-    b_variable = eval(file.read())
-    file.close()
-
-    if not isinstance(b_variable, np.ndarray):
-        b_variable = np.array(b_variable)
-    if not isinstance(a_variable, np.ndarray):
-        a_variable = np.array(a_variable)
+    a_variable = np.load(a_file)
+    b_variable = np.load(b_file)
 
     if b_variable.shape != a_variable.shape:
         raise ValueError(
diff --git a/examples/open_clip/test.py b/examples/open_clip/test.py
index 99ea731c8..1306c6a49 100644
--- a/examples/open_clip/test.py
+++ b/examples/open_clip/test.py
@@ -12,6 +12,7 @@
 import os
 import sys
 
+import numpy as np
 from PIL import Image
 from src.open_clip import create_model_and_transforms, get_tokenizer
 
@@ -84,30 +85,16 @@ def main(args):
         root = "./" + args.model_name + args.pretrained
         os.mkdir(root)
 
-    # file = open(root + "/image.txt", "w+")
-    # file.write(str(image.asnumpy().tolist()))
-    # file.close()
-    #
-    # file = open(root + "/text.txt", "w+")
-    # file.write(str(text.asnumpy().tolist()))
-    # file.close()
-
-    file = open(root + "/image_features.txt", "w+")
-    file.write(str(image_features.asnumpy().tolist()))
-    file.close()
-
-    file = open(root + "/text_features.txt", "w+")
-    file.write(str(text_features.asnumpy().tolist()))
-    file.close()
+    # save as np files.
+    np.save(os.path.join(root, "image_features.npy"), image_features.asnumpy())
+    np.save(os.path.join(root, "text_features.npy"), text_features.asnumpy())
 
     image_features /= image_features.norm(dim=-1, keepdim=True)
     text_features /= text_features.norm(dim=-1, keepdim=True)
 
     text_probs = ops.softmax(100.0 * image_features @ text_features.T, axis=-1)
 
-    file = open(root + "/text_probs.txt", "w+")
-    file.write(str(text_probs.asnumpy().tolist()))
-    file.close()
+    np.save(os.path.join(root, "text_probs.npy"), text_probs.asnumpy())
 
 
 if __name__ == "__main__":