diff --git a/llmfoundry/callbacks/hf_checkpointer.py b/llmfoundry/callbacks/hf_checkpointer.py
index 65bdcb3b6c..4365a5b2e5 100644
--- a/llmfoundry/callbacks/hf_checkpointer.py
+++ b/llmfoundry/callbacks/hf_checkpointer.py
@@ -588,9 +588,10 @@ def tensor_hook(
                     del new_base_model_instance
                 else:
                     new_model_instance = type(original_model)(new_config)
-                    new_model_instance.generation_config.update(
-                        **original_model.generation_config.to_dict(),
-                    )
+                    if new_model_instance.generation_config is not None:
+                        new_model_instance.generation_config.update(
+                            **original_model.generation_config.to_dict(),
+                        )
 
             # Then load the state dict in with "assign" so that the state dict
             # is loaded properly even though the model is initially on meta device.
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 66ec739a65..bf5f2a970b 100644
--- a/tests/a_scripts/inference/test_convert_composer_to_hf.py
+++ b/tests/a_scripts/inference/test_convert_composer_to_hf.py
@@ -8,13 +8,14 @@
 import pathlib
 import shutil
 from argparse import Namespace
-from typing import Any, Callable, Optional, cast
+from typing import Any, Callable, Optional, Union, cast
 from unittest import mock
 from unittest.mock import ANY, MagicMock, patch
 
 import catalogue
 import pytest
 import torch
+import torch.nn as nn
 import transformers
 from composer import ComposerModel, Trainer
 from composer.loggers import MLFlowLogger
@@ -23,7 +24,13 @@
 from omegaconf import OmegaConf as om
 from torch.distributed._tensor.api import DTensor
 from torch.utils.data import DataLoader
-from transformers import PreTrainedModel, PreTrainedTokenizerBase
+from transformers import (
+    AutoConfig,
+    GenerationConfig,
+    PretrainedConfig,
+    PreTrainedModel,
+    PreTrainedTokenizerBase,
+)
 
 from llmfoundry.callbacks import HuggingFaceCheckpointer
 from llmfoundry.callbacks.hf_checkpointer import _maybe_get_license_filename
@@ -1637,3 +1644,48 @@ def test_license_file_finder(
     found_path = _maybe_get_license_filename(str(tmp_path))
     assert (found_path == license_file_name
            ) if license_file_name is not None else (found_path is None)
+
+
+@pytest.mark.parametrize('generation_config', [None, {}, {'max_length': 200}])
+def test_generation_config_variants(
+    generation_config: Optional[Union[dict[str, Any], GenerationConfig]],
+):
+
+    class MockModel(nn.Module):
+
+        def __init__(self, config: PretrainedConfig):
+            super().__init__()
+            self.config = config
+            # Ensure generation_config is always a GenerationConfig object
+            if isinstance(config.generation_config, dict):
+                self.generation_config = GenerationConfig(
+                    **config.generation_config,
+                )
+            else:
+                self.generation_config = config.generation_config
+
+    config = AutoConfig.from_pretrained('gpt2')
+    # Convert dict to GenerationConfig if needed
+    if isinstance(generation_config, dict):
+        generation_config = GenerationConfig(**generation_config)
+    config.generation_config = generation_config
+
+    mock_model = MockModel(config)
+    logger = MagicMock()
+    state = MagicMock()
+    state.timestamp.batch = 1
+    state.is_model_ddp = False
+    state.model.model = mock_model
+    state.model.tokenizer = None
+
+    checkpointer = HuggingFaceCheckpointer(
+        save_folder='test',
+        save_interval='1ba',
+    )
+
+    checkpointer._save_checkpoint(
+        state=state,
+        logger=logger,
+        upload_to_save_folder=False,
+        register_to_mlflow=False,
+    )