diff --git a/tests/models/opt/test_modeling_opt.py b/tests/models/opt/test_modeling_opt.py
index 7a844da559ac5b..e9959255d9f8dd 100644
--- a/tests/models/opt/test_modeling_opt.py
+++ b/tests/models/opt/test_modeling_opt.py
@@ -22,7 +22,7 @@
 import timeout_decorator  # noqa
 
 from transformers import OPTConfig, is_torch_available
-from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
+from transformers.testing_utils import require_torch, slow, torch_device
 
 from ...generation.test_generation_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
@@ -266,25 +266,21 @@ def _long_tensor(tok_lst):
 
 
 @require_torch
-@require_sentencepiece
-@require_tokenizers
 class OPTModelIntegrationTests(unittest.TestCase):
     @slow
     def test_inference_no_head(self):
         model = OPTModel.from_pretrained("facebook/opt-350m").to(torch_device)
         input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
-        attention_mask = input_ids.ne(model.config.pad_token_id)
         with torch.no_grad():
-            output = model(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state
+            output = model(input_ids=input_ids).last_hidden_state
         expected_shape = torch.Size((1, 11, 512))
         self.assertEqual(output.shape, expected_shape)
         expected_slice = torch.tensor(
-            [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]], device=torch_device
+            [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]], device=torch_device
         )
         self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3))
 
 
-@require_tokenizers
 @require_torch
 @slow
 class OPTEmbeddingsTest(unittest.TestCase):