diff --git a/auto_transformers.py b/auto_transformers.py
index c032711..46ab490 100644
--- a/auto_transformers.py
+++ b/auto_transformers.py
@@ -76,24 +76,25 @@ class AutoTransformers(NNOperator):
         vec = features.detach().numpy()
         return vec
 
-    def save_model(self, jit: bool = True, destination: str = 'default'):
-        if destination == 'default':
+    def save_model(self, format: str = 'default', path: str = 'default'):
+        if path == 'default':
             path = str(Path(__file__).parent)
-            destination = os.path.join(path, self.model_name + '.pt')
+            name = self.model_name.replace('/', '-')
+            path = os.path.join(path, name + '.pt')
         inputs = self.tokenizer('[CLS]', return_tensors='pt')
         inputs = list(inputs.values())
-        if jit:
+        if format == 'torchscript':
             try:
                 try:
-                    traced_model = torch.jit.script(self.model)
+                    jit_model = torch.jit.script(self.model)
                 except Exception:
-                    traced_model = torch.jit.trace(self.model, inputs, strict=False)
-                torch.jit.save(traced_model, destination)
+                    jit_model = torch.jit.trace(self.model, inputs, strict=False)
+                torch.jit.save(jit_model, path)
             except Exception as e:
                 log.error(f'Fail to save as torchscript: {e}.')
                 raise RuntimeError(f'Fail to save as torchscript: {e}.')
         else:
-            torch.save(self.model, destination)
+            torch.save(self.model, path)
 
 
 def get_model_list():
diff --git a/test_save.py b/test_save.py
index 3cf21d0..5b4d2ae 100644
--- a/test_save.py
+++ b/test_save.py
@@ -2,14 +2,19 @@ from auto_transformers import AutoTransformers
 
 import torch
 
-models = ['bert-base-cased', 'distilbert-base-cased', 'distilgpt2']
+models = [
+    'bert-base-cased',
+    'distilbert-base-cased',
+    'distilgpt2',
+    'google/fnet-base'
+]
 
 for name in models:
     try:
         op = AutoTransformers(model_name=name)
         out1 = op('hello, world.')
-        op.save_model()
-        op.model = torch.jit.load(name + '.pt')
+        op.save_model(format='torchscript')
+        op.model = torch.jit.load(name.replace('/', '-') + '.pt')
         out2 = op('hello, world.')
         assert (out1 == out2).all()
         print(f'[SUCCESS] Saved torchscript for model "{name}"')