Update [release]

Author: AjayP13

src/trainers/_train_hf_base.py

@@ -7,6 +7,7 @@
 from typing import TYPE_CHECKING, Any, Type, cast
 
 import torch
+
 from datasets.fingerprint import Hasher
 
 from .. import DataDreamer
@@ -489,9 +490,9 @@ def export_to_disk(self, path: str, adapter_only: bool = False) -> PreTrainedMod
         from .train_hf_finetune import TrainHFFineTune
         from .train_setfit_classifier import TrainSetFitClassifier
 
-        assert (
-            not adapter_only or self.peft_config
-        ), "`adapter_only` can only be used if a `peft_config` was provided."
+        assert not adapter_only or self.peft_config, (
+            "`adapter_only` can only be used if a `peft_config` was provided."
+        )
 
         # Clear the directory
         clear_dir(path)
@@ -624,9 +625,9 @@ def publish_to_hf_hub(  # noqa: C901
         from .train_hf_finetune import TrainHFFineTune
         from .train_setfit_classifier import TrainSetFitClassifier
 
-        assert (
-            not adapter_only or self.peft_config
-        ), "`adapter_only` can only be used if a `peft_config` was provided."
+        assert not adapter_only or self.peft_config, (
+            "`adapter_only` can only be used if a `peft_config` was provided."
+        )
 
         # Login
         api = hf_hub_login(token=token)
@@ -759,9 +760,9 @@ def publish_to_hf_hub(  # noqa: C901
             f""" The training arguments can be found [here](training_args.json)."""
         )
         if os.path.exists(self.model_name) and os.path.isdir(self.model_name):
-            readme_contents = readme_contents.replace("{base_model}", self.model_name)
-        else:
             readme_contents = readme_contents.replace("\nbase_model: {base_model}", "")
+        else:
+            readme_contents = readme_contents.replace("{base_model}", self.model_name)
         tags = tags + model_names
         tags = (
             tags