rebase on peft

python/flexflow/serve/serve.py

@@ -406,14 +406,21 @@ def __load_hf_weights(self):
 
     def upload_hf_model(self, new_model_id: str, model_path:str, private: bool = False):
         """
+<<<<<<< HEAD
         Uploads the model to the Hugging Face Hub, with reverse conversion of weights.
+=======
+        Uploads the model weights to the Hugging Face Hub, with reverse conversion of weights.
+>>>>>>> cdf24eb0 (modify upload logic and add reconvert functions for opt models)
         
         :param new_model_id: The new model ID for the Hugging Face Hub.
         :param model_path: The path where the FlexFlow weights are stored.
         :param private: Whether to upload the model as a private model.
         """
         print(f"Preparing model for upload to Hugging Face Hub: {new_model_id}")
+<<<<<<< HEAD
         print("tokenizer path is: ", self.tokenizer_path)
+=======
+>>>>>>> cdf24eb0 (modify upload logic and add reconvert functions for opt models)
 
         # Initialize a new Hugging Face model instance
         hf_model = AutoModelForCausalLM.from_config(self.hf_config)
@@ -430,6 +437,7 @@ def upload_hf_model(self, new_model_id: str, model_path:str, private: bool = Fal
         temp_dir = tempfile.mkdtemp()
         hf_model.save_pretrained(temp_dir)
 
+<<<<<<< HEAD
         # Copy the tokenizer files to the temporary directory
         tokenizer_files = [f for f in os.listdir(self.tokenizer_path)]
         for file_name in tokenizer_files:
@@ -440,6 +448,8 @@ def upload_hf_model(self, new_model_id: str, model_path:str, private: bool = Fal
         if os.path.exists(rev_sha_path):
             os.remove(rev_sha_path)
 
+=======
+>>>>>>> cdf24eb0 (modify upload logic and add reconvert functions for opt models)
         # Ensure Hugging Face CLI is logged in
         if not HfFolder.get_token():
             print("Hugging Face token not found. Please login using `huggingface-cli login`.")