Update utils.py

utils.py

@@ -160,6 +160,7 @@ class MolecularPropertyPredictionModel():
             num_labels=1,
             finetuning_task="classification", # this is not about our task type
             trust_remote_code=True,
+            auth_token = os.environ.get("TOKEN_FROM_SECRET") or True
         )
 
         self.base_model = AutoModelForSequenceClassification.from_pretrained(
@@ -167,12 +168,14 @@ class MolecularPropertyPredictionModel():
             config=config,
             device_map="cpu",
             trust_remote_code=True,
+            auth_token = os.environ.get("TOKEN_FROM_SECRET") or True
         )
 
         # load the tokenizer
         self.tokenizer = AutoTokenizer.from_pretrained(
             "ChemFM/admet_ppbr_az",
             trust_remote_code=True,
+            auth_token = os.environ.get("TOKEN_FROM_SECRET") or True
         )
         special_tokens_dict = dict(pad_token=DEFAULT_PAD_TOKEN)
         smart_tokenizer_and_embedding_resize(
@@ -201,9 +204,9 @@ class MolecularPropertyPredictionModel():
         # switch adapter
         try:
             self.adapter_name = adapter_name
-            self.lora_model = PeftModel.from_pretrained(self.base_model, adapter_id)
+            self.lora_model = PeftModel.from_pretrained(self.base_model, adapter_id, auth_token = os.environ.get("TOKEN_FROM_SECRET") or True)
             if adapter_name not in self.apapter_scaler_path:
-                self.apapter_scaler_path[adapter_name] = hf_hub_download(adapter_id, filename="scaler.pkl")
+                self.apapter_scaler_path[adapter_name] = hf_hub_download(adapter_id, filename="scaler.pkl", auth_token = os.environ.get("TOKEN_FROM_SECRET") or True)
             if os.path.exists(self.apapter_scaler_path[adapter_name]):
                 self.scaler = pickle.load(open(self.apapter_scaler_path[adapter_name], "rb"))
             else: