diff --git a/composer/models/huggingface.py b/composer/models/huggingface.py
index 2dcb7402e8..5fddf42510 100644
--- a/composer/models/huggingface.py
+++ b/composer/models/huggingface.py
@@ -501,10 +501,7 @@ def eval_forward(self, batch, outputs: Optional[Any] = None):
 
             # HF encoder decoder models like T5 expect either decoder_input_ids or labels,
             # so we add decoder_input_ids to the batch if it is missing
-            from transformers import PretrainedConfig
-            assert isinstance(self.model.config, PretrainedConfig)
-            model_config: PretrainedConfig = self.model.config
-            if model_config.is_encoder_decoder and 'decoder_input_ids' not in batch:
+            if self.config.is_encoder_decoder and 'decoder_input_ids' not in batch:
                 if hasattr(self.model, 'prepare_decoder_input_ids_from_labels'):
                     batch['decoder_input_ids'] = self.model.prepare_decoder_input_ids_from_labels(labels=self.labels)
                 else:
@@ -561,9 +558,7 @@ def get_metadata(self):
             model_dir = tmp_dir / 'model'
             tokenizer_dir = tmp_dir / 'tokenizer'
 
-            from transformers import PretrainedConfig
-            assert isinstance(self.model.config, PretrainedConfig)
-            original_model_config: PretrainedConfig = self.model.config
+            original_model_config: PretrainedConfig = self.config
             original_model_config.save_pretrained(model_dir)
             if self.tokenizer is not None:
                 self.tokenizer.save_pretrained(tokenizer_dir)
@@ -645,10 +640,7 @@ def generate(self, input_ids: torch.Tensor, **kwargs):
         if not using_torch_2() and not self.dummy_forward_called and is_model_fsdp(self.model):
             with torch.no_grad():
                 maybe_decoder_input_ids = {}
-                from transformers import PretrainedConfig
-                assert isinstance(self.model.config, PretrainedConfig)
-                model_config: PretrainedConfig = self.model.config
-                if model_config.is_encoder_decoder:
+                if self.config.is_encoder_decoder:
                     maybe_decoder_input_ids['decoder_input_ids'] = torch.tensor([[0]],
                                                                                 dtype=torch.long,
                                                                                 device=input_ids.device)