diff --git a/tests/transformers/tests/test_modeling_common.py b/tests/transformers/tests/test_modeling_common.py
index d33cf1e58d..c2a818f257 100755
--- a/tests/transformers/tests/test_modeling_common.py
+++ b/tests/transformers/tests/test_modeling_common.py
@@ -83,6 +83,7 @@
 
 if is_torch_available():
     import torch
+    from safetensors.torch import save_file as safe_save_file
     from torch import nn
     from transformers import MODEL_MAPPING, AdaptiveEmbedding
     from transformers.pytorch_utils import id_tensor_storage
@@ -408,7 +409,7 @@ class CopyClass(base_class):
 
             # check that certain keys didn't get saved with the model
             with tempfile.TemporaryDirectory() as tmpdirname:
-                model.config.save_pretrained(tmpdirname)
+                model.save_pretrained(tmpdirname)
                 torch.save(state_dict, os.path.join(tmpdirname, "pytorch_model.bin"))
 
                 model_fast_init = base_class_copy.from_pretrained(tmpdirname)
@@ -1661,8 +1662,8 @@ def test_model_weights_reload_no_missing_tied_weights(self):
 
                 # We are nuking ALL weights on file, so every parameter should
                 # yell on load. We're going to detect if we yell too much, or too little.
-                with open(os.path.join(tmp_dir, "pytorch_model.bin"), "wb") as f:
-                    torch.save({}, f)
+                placeholder_dict = {"tensor": torch.tensor([1, 2])}
+                safe_save_file(placeholder_dict, os.path.join(tmp_dir, "model.safetensors"), metadata={"format": "pt"})
                 model_reloaded, infos = model_class.from_pretrained(tmp_dir, output_loading_info=True)
 
                 prefix = f"{model_reloaded.base_model_prefix}."