diff --git a/tests/a_scripts/inference/test_convert_composer_to_hf.py b/tests/a_scripts/inference/test_convert_composer_to_hf.py
index d21c942dee..5c3d0f1830 100644
--- a/tests/a_scripts/inference/test_convert_composer_to_hf.py
+++ b/tests/a_scripts/inference/test_convert_composer_to_hf.py
@@ -382,13 +382,17 @@ def test_huggingface_conversion_callback_interval(
     'hf_save_interval,save_interval,max_duration,expected_hf_checkpoints,expected_normal_checkpoints',
     [('1ba', '1ba', '1ba', 1, 1)])
 @patch('os.cpu_count', MagicMock(return_value=None))
-def test_huggingface_conversion_callback(model: str, tmp_path: pathlib.Path,
-                                         tie_word_embeddings: bool,
-                                         fsdp_state_dict_type: Optional[str],
-                                         hf_save_interval: str,
-                                         save_interval: str, max_duration: str,
-                                         expected_hf_checkpoints: int,
-                                         expected_normal_checkpoints: int):
+def test_huggingface_conversion_callback(
+    model: str,
+    tmp_path: pathlib.Path,
+    tie_word_embeddings: bool,
+    fsdp_state_dict_type: Optional[str],
+    hf_save_interval: str,
+    save_interval: str,
+    max_duration: str,
+    expected_hf_checkpoints: int,
+    expected_normal_checkpoints: int,
+):
     delete_transformers_cache()
 
     dist.initialize_dist(get_device('gpu'))
@@ -580,12 +584,15 @@ def test_huggingface_conversion_callback(model: str, tmp_path: pathlib.Path,
             assert len(normal_checkpoints) == expected_normal_checkpoints
             assert len(huggingface_checkpoints) == expected_hf_checkpoints
 
-            # Load the last huggingface checkpoint
-            loaded_model = transformers.AutoModelForCausalLM.from_pretrained(
-                os.path.join(tmp_path, 'checkpoints', 'huggingface',
-                             f'ba{batches_per_epoch}'),
-                trust_remote_code=True,
-            )
+            # Patch flash_attn package to be empty to simulate loading the model in
+            # an environment without flash atttention installed
+            with patch.dict('sys.modules', {'flash_attn': None}):
+                # Load the last huggingface checkpoint
+                loaded_model = transformers.AutoModelForCausalLM.from_pretrained(
+                    os.path.join(tmp_path, 'checkpoints', 'huggingface',
+                                 f'ba{batches_per_epoch}'),
+                    trust_remote_code=True,
+                )
 
             # Check that the loaded model has the correct precision, and then set it back
             # to the original for the equivalence check