diff --git a/llmfoundry/callbacks/hf_checkpointer.py b/llmfoundry/callbacks/hf_checkpointer.py
index a219b98ddb..614ac4314b 100644
--- a/llmfoundry/callbacks/hf_checkpointer.py
+++ b/llmfoundry/callbacks/hf_checkpointer.py
@@ -4,11 +4,11 @@
 import contextlib
 import copy
 import logging
+import math
 import os
 import tempfile
 from pathlib import Path
 from typing import Optional, Union
-import math
 
 import torch
 from composer.core import Callback, Event, State, Time, TimeUnit
@@ -238,8 +238,11 @@ def _save_checkpoint(self, state: State, logger: Logger):
                 # we need a special case to identify we are on the last batch and should write the mlflow checkpoint
                 is_last_batch = False
                 if self.save_interval.unit == TimeUnit.DURATION and self.save_interval.value == 1 and state.max_duration.unit == TimeUnit.EPOCH:
-                    is_last_batch = int(state.timestamp.batch) % math.ceil(state.max_duration.value * state.dataloader_len) == 0
-                if self.mlflow_registered_model_name is not None and ((elapsed_duration is not None and elapsed_duration >= 1.0) or is_last_batch):
+                    is_last_batch = int(state.timestamp.batch) % math.ceil(
+                        state.max_duration.value * state.dataloader_len) == 0
+                if self.mlflow_registered_model_name is not None and (
+                    (elapsed_duration is not None and
+                     elapsed_duration >= 1.0) or is_last_batch):
                     components = {'model': new_model_instance}
                     if original_tokenizer is not None:
                         components['tokenizer'] = original_tokenizer
diff --git a/tests/test_hf_conversion_script.py b/tests/test_hf_conversion_script.py
index 71c35f3723..d2f203d3a0 100644
--- a/tests/test_hf_conversion_script.py
+++ b/tests/test_hf_conversion_script.py
@@ -251,13 +251,14 @@ def test_callback_inits_with_defaults():
 @pytest.mark.parametrize('model', ['mpt', 'neo', 'llama2'])
 @pytest.mark.parametrize('fsdp_state_dict_type', ['full', 'sharded', None])
 @pytest.mark.parametrize('log_to_mlflow', [True, False])
-@pytest.mark.parametrize('hf_save_interval,save_interval,max_duration,expected_hf_checkpoints,expected_normal_checkpoints', [('3ba', '2ba', '7ba', 3, 4), ('1dur', '2ba', '1ep', 1, 4)])
+@pytest.mark.parametrize(
+    'hf_save_interval,save_interval,max_duration,expected_hf_checkpoints,expected_normal_checkpoints',
+    [('3ba', '2ba', '7ba', 3, 4), ('1dur', '2ba', '1ep', 1, 4)])
 def test_huggingface_conversion_callback(model: str, tmp_path: pathlib.Path,
                                          fsdp_state_dict_type: Optional[str],
                                          log_to_mlflow: bool,
                                          hf_save_interval: str,
-                                         save_interval: str,
-                                         max_duration: str,
+                                         save_interval: str, max_duration: str,
                                          expected_hf_checkpoints: int,
                                          expected_normal_checkpoints: int):
     delete_transformers_cache()
@@ -269,7 +270,7 @@ def test_huggingface_conversion_callback(model: str, tmp_path: pathlib.Path,
     dataset_size = 14
     precision_str = 'bfloat16'
     precision = torch.bfloat16
-    batches_per_epoch = math.ceil(dataset_size / (device_batch_size*2))
+    batches_per_epoch = math.ceil(dataset_size / (device_batch_size * 2))
 
     checkpointer_callback = HuggingFaceCheckpointer(
         save_folder=os.path.join(tmp_path, 'checkpoints'),