diff --git a/src/modalities/models/huggingface/huggingface_models.py b/src/modalities/models/huggingface/huggingface_models.py
index b80f222d..9a1d5b93 100644
--- a/src/modalities/models/huggingface/huggingface_models.py
+++ b/src/modalities/models/huggingface/huggingface_models.py
@@ -2,8 +2,9 @@
 from typing import Any, Dict, List, Optional
 
 import torch
-from pydantic import BaseModel
-from transformers import AutoModelForCausalLM, AutoModelForMaskedLM, AutoTokenizer, PreTrainedTokenizer
+
+from pydantic import BaseModel, ConfigDict
+from transformers import AutoModelForCausalLM, AutoModelForMaskedLM, AutoTokenizer
 
 from modalities.config.lookup_enum import LookupEnum
 from modalities.models.model import NNModel
@@ -36,6 +37,10 @@ class HuggingFacePretrainedModelConfig(BaseModel):
     model_args: Optional[Any] = None
     kwargs: Optional[Any] = None
 
+    # avoid warning about protected namespace 'model_', see
+    # https://docs.pydantic.dev/2.7/api/config/#pydantic.config.ConfigDict.protected_namespaces
+    model_config = ConfigDict(protected_namespaces=())
+
 
 class HuggingFacePretrainedModel(NNModel):
 
diff --git a/src/modalities/tokenization/tokenizer_wrapper.py b/src/modalities/tokenization/tokenizer_wrapper.py
index 1e413a92..f419771e 100644
--- a/src/modalities/tokenization/tokenizer_wrapper.py
+++ b/src/modalities/tokenization/tokenizer_wrapper.py
@@ -19,9 +19,11 @@ def vocab_size(self) -> int:
     def get_token_id(self, token: str) -> int:
         raise NotImplementedError
 
+
     def get_token_id(self, token: str) -> int:
         raise NotImplementedError
 
+
 class PreTrainedHFTokenizer(TokenizerWrapper):
     def __init__(
         self, pretrained_model_name_or_path: str, max_length: int, truncation: bool = True, padding: str = "max_length"
@@ -44,10 +46,12 @@ def tokenize(self, text: str) -> List[int]:
         )["input_ids"]
         return tokens
 
+
     def decode(self, token_ids: List[int]) -> str:
         decoded_text = self.tokenizer.decode(token_ids)
         return decoded_text
 
+
     def get_token_id(self, token: str) -> int:
         token_id = self.tokenizer.convert_tokens_to_ids(token)
         if isinstance(token_id, list):