diff --git a/src/pytorch_ie/core/hf_hub_mixin.py b/src/pytorch_ie/core/hf_hub_mixin.py
index 4dc2a316..bc49d2cb 100644
--- a/src/pytorch_ie/core/hf_hub_mixin.py
+++ b/src/pytorch_ie/core/hf_hub_mixin.py
@@ -4,7 +4,6 @@
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Type, TypeVar, Union
 
-# TODO: fix ignore
 import requests  # type: ignore
 import torch
 from huggingface_hub.constants import CONFIG_NAME, PYTORCH_WEIGHTS_NAME
@@ -19,7 +18,7 @@
 MODEL_CONFIG_TYPE_KEY = "model_type"
 TASKMODULE_CONFIG_TYPE_KEY = "taskmodule_type"
 
-# Generic variable that is either ModelHubMixin or a subclass thereof
+# Generic variable that is either PieBaseHFHubMixin or a subclass thereof
 T = TypeVar("T", bound="PieBaseHFHubMixin")
 
 
@@ -32,28 +31,24 @@ class PieBaseHFHubMixin:
     of mixin integration with the Hub. Check out our [integration guide](../guides/integrations) for more instructions.
     """
 
-    config_name = MODEL_CONFIG_NAME  # @ArneBinder
-    config_type_key = MODEL_CONFIG_TYPE_KEY  # @ArneBinder
+    config_name = MODEL_CONFIG_NAME
+    config_type_key = MODEL_CONFIG_TYPE_KEY
 
-    def __init__(self, *args, is_from_pretrained: bool = False, **kwargs):  # @ArneBinder
-        super().__init__(*args, **kwargs)  # @ArneBinder
-        self._is_from_pretrained = is_from_pretrained  # @ArneBinder
+    def __init__(self, *args, is_from_pretrained: bool = False, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._is_from_pretrained = is_from_pretrained
 
     @property
-    def is_from_pretrained(self):  # @ArneBinder
-        return self._is_from_pretrained  # @ArneBinder
+    def is_from_pretrained(self):
+        return self._is_from_pretrained
 
-    # def _config_name(self) -> Optional[str]:  # @ArneBinder
-    #    return None  # @ArneBinder
-
-    def _config(self) -> Optional[Dict[str, Any]]:  # @ArneBinder
-        return None  # @ArneBinder
+    def _config(self) -> Optional[Dict[str, Any]]:
+        return None
 
     def save_pretrained(
         self,
         save_directory: Union[str, Path],
         *,
-        # config: Optional[dict] = None,  # config: Optional[dict] = None @ArneBinder
         repo_id: Optional[str] = None,
         push_to_hub: bool = False,
         **kwargs,
@@ -64,8 +59,6 @@ def save_pretrained(
         Args:
             save_directory (`str` or `Path`):
                 Path to directory in which the model weights and configuration will be saved.
-            config (`dict`, *optional*):
-                Model configuration specified as a key/value dictionary.
             push_to_hub (`bool`, *optional*, defaults to `False`):
                 Whether or not to push your model to the Huggingface Hub after saving it.
             repo_id (`str`, *optional*):
@@ -81,7 +74,7 @@ def save_pretrained(
         self._save_pretrained(save_directory)
 
         # saving config
-        config = self._config()  # @ArneBinder
+        config = self._config()
         if isinstance(config, dict):
             (save_directory / self.config_name).write_text(json.dumps(config, indent=2))
 
@@ -179,7 +172,7 @@ def from_pretrained(
 
         # The value of is_from_pretrained is set to True when the model is loaded from pretrained.
         # Note that the value may be already available in model_kwargs.
-        model_kwargs["is_from_pretrained"] = True  # @ArneBinder
+        model_kwargs["is_from_pretrained"] = True
 
         return cls._from_pretrained(
             model_id=str(model_id),
@@ -376,7 +369,7 @@ def _from_pretrained(
     ) -> "PieModelHFHubMixin":
         """Load Pytorch pretrained weights and return the loaded model."""
         if os.path.isdir(model_id):
-            print("Loading weights from local directory")
+            logger.info("Loading weights from local directory")
             model_file = os.path.join(model_id, PYTORCH_WEIGHTS_NAME)
         else:
             model_file = hf_hub_download(