diff --git a/llmfoundry/callbacks/hf_checkpointer.py b/llmfoundry/callbacks/hf_checkpointer.py
index 6098bbeefd..8956858a93 100644
--- a/llmfoundry/callbacks/hf_checkpointer.py
+++ b/llmfoundry/callbacks/hf_checkpointer.py
@@ -239,6 +239,7 @@ def __init__(
                 +
                 f'Defaulting to final_register_only=False and saving the HuggingFace checkpoint to {save_folder=}.',
             )
+        self.use_mlflow_log_model = False
 
         # mlflow config setup
         if mlflow_logging_config is None:
@@ -269,6 +270,8 @@ def __init__(
                 'input_example',
                 default_input_example,
             )
+        if mlflow_logging_config['use_mlflow_log_model']:
+            self.use_mlflow_log_model = True
 
         self.mlflow_logging_config = mlflow_logging_config
         if 'metadata' in self.mlflow_logging_config:
@@ -709,7 +712,7 @@ def tensor_hook(
                 log.debug('Logging Hugging Face model to MLFlow')
                 for i, mlflow_logger in enumerate(self.mlflow_loggers):
                     log.debug(
-                        f'Logging model to UC at {mlflow_logger.model_registry_prefix}.{self.mlflow_registered_model_name}',
+                        f'Registering model to UC at {mlflow_logger.model_registry_prefix}.{self.mlflow_registered_model_name}',
                     )
                     local_save_path = str(
                         Path(temp_save_dir) / f'mlflow_save_{i}',
@@ -743,7 +746,7 @@ def tensor_hook(
                             'transformers',
                             'torch',
                         ]
-                        mlflow_logger.log_model(**model_saving_kwargs)
+                        mlflow_logger.save_model(**model_saving_kwargs)
 
                     # Upload the license file generated by mlflow during the model saving.
                     license_filename = _maybe_get_license_filename(
@@ -767,8 +770,7 @@ def tensor_hook(
 
                     # Spawn a new process to register the model.
                     # Slower method to register the model via log_model.
-                    # TODO: design this with some extra param in the model saving config to invoke this
-                    if True:
+                    if self.use_mlflow_log_model:
                         process = SpawnProcess(
                             target=_log_model_multiprocess,
                             kwargs={