diff --git a/src/postprocessing/prepare_upload.py b/src/postprocessing/prepare_upload.py
index a1508ff..3abb9e5 100644
--- a/src/postprocessing/prepare_upload.py
+++ b/src/postprocessing/prepare_upload.py
@@ -10,8 +10,6 @@
 
 import torch
 
-from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
-
 from safetensors.torch import save_file
 
 from tqdm import tqdm
@@ -48,9 +46,15 @@ def prepare_upload(
                 )
             model_state_dict[k] = v
 
-    original_model = AutoModelForCausalLM.from_pretrained(config.pretrained_model_name)
-    original_model.load_state_dict(model_state_dict)
-    state_dict = original_model.state_dict()
+    state_dict = model_state_dict
+    if config.precision == 32 or config.precision == "32":
+        safetensors_dtype = torch.float32
+    elif config.precision == 16 or config.precision == "16":
+        safetensors_dtype = torch.float16
+    elif config.precision == "bf16":
+        safetensors_dtype = torch.bfloat16
+    else:
+        raise ValueError(f"Invalid precision type: {config.precision}")
     keys = list(state_dict.keys())
     num_splits = config.num_safetensors
     split_size = len(keys) // num_splits
@@ -72,7 +76,8 @@ def prepare_upload(
     for i in tqdm(range(num_splits)):
         safe_tensors_name = f"model-{i+1:05d}-of-{num_splits:05d}.safetensors"
         part_state_dict = {
-            k: state_dict[k] for k in keys[i * split_size : (i + 1) * split_size]
+            k: state_dict[k].to(safetensors_dtype)
+            for k in keys[i * split_size : (i + 1) * split_size]
         }
         part_state_dict_mapping = {
             k: safe_tensors_name for k in keys[i * split_size : (i + 1) * split_size]
@@ -91,15 +96,6 @@ def prepare_upload(
             f,
             indent=2,
         )
-    tokenizer = AutoTokenizer.from_pretrained(config.pretrained_model_name)
-    tokenizer.save_pretrained(save_dir)
-    model_config = AutoConfig.from_pretrained(config.pretrained_model_name)
-    model_config._name_or_path = (
-        f"{config.user_name}/{config.model_type}-{config.upload_tag}"
-    )
-    if config.strategy.startswith("deepspeed"):
-        model_config.torch_dtype = "float32"
-    model_config.save_pretrained(save_dir)
 
 
 if __name__ == "__main__":