diff --git a/sdk/python/kubeflow/training/training_container/hf_llm_training.py b/sdk/python/kubeflow/training/training_container/hf_llm_training.py
index 4c44243d51..6a8749570f 100644
--- a/sdk/python/kubeflow/training/training_container/hf_llm_training.py
+++ b/sdk/python/kubeflow/training/training_container/hf_llm_training.py
@@ -5,7 +5,7 @@
     AutoConfig,
     TrainingArguments,
     DataCollatorForLanguageModeling,
-    Trainer
+    Trainer,
 )
 import torch
 from datasets import load_dataset
@@ -14,13 +14,15 @@
 
 def setup_model_and_tokenizer(token_dir, model_dir):
     # Set up the model and tokenizer
-    tokenizer = AutoTokenizer.from_pretrained(token_dir, use_fast=False, trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained(
+        token_dir, use_fast=False, trust_remote_code=True
+    )
     tokenizer.pad_token = tokenizer.eos_token
     tokenizer.add_pad_token = True
 
     model = AutoModelForCausalLM.from_pretrained(
         model_dir,
-        device_map='auto',
+        device_map="auto",
         trust_remote_code=True,
     )
 
@@ -30,25 +32,30 @@ def setup_model_and_tokenizer(token_dir, model_dir):
 
     return model, tokenizer
 
+
 def load_and_preprocess_data(dataset_dir, tokenizer):
     # Load and preprocess the dataset
-    train_data = load_dataset(dataset_dir, split='train').map(lambda x: tokenizer(x['text']), batched=True)
+    train_data = load_dataset(dataset_dir, split="train").map(
+        lambda x: tokenizer(x["text"]), batched=True
+    )
     train_data = train_data.train_test_split(shuffle=True, test_size=200)
 
     try:
-        eval_data = load_dataset(dataset_dir, split='eval')
+        eval_data = load_dataset(dataset_dir, split="eval")
     except Exception as err:
         eval_data = None
 
     return train_data, eval_data
 
+
 def setup_peft_model(model, lora_config):
     # Set up the PEFT model
     lora_config = LoraConfig(**lora_config)
     model = get_peft_model(model, lora_config)
     return model
 
-def train_model(model, train_data, eval_data,tokenizer, train_params):
+
+def train_model(model, train_data, eval_data, tokenizer, train_params):
     # Train the model
     trainer = Trainer(
         model=model,
@@ -58,26 +65,27 @@ def train_model(model, train_data, eval_data,tokenizer, train_params):
         args=TrainingArguments(
             **train_params,
             data_collator=DataCollatorForLanguageModeling(
-                tokenizer,
-                pad_to_multiple_of=8,
-                return_tensors="pt",
-                mlm=False
+                tokenizer, pad_to_multiple_of=8, return_tensors="pt", mlm=False
             )
-        )
+        ),
     )
 
     trainer.train()
 
+
 def parse_arguments():
-    parser = argparse.ArgumentParser(description='Script for training a model with PEFT configuration.')
-    parser.add_argument('--model_dir', help='directory containing model')
-    parser.add_argument('--token_dir', help='directory containing tokenizer')
-    parser.add_argument('--dataset_dir', help='directory contaning dataset')
-    parser.add_argument('--peft_config', help='peft_config')
-    parser.add_argument('--train_params', help='hugging face training parameters')
+    parser = argparse.ArgumentParser(
+        description="Script for training a model with PEFT configuration."
+    )
+    parser.add_argument("--model_dir", help="directory containing model")
+    parser.add_argument("--token_dir", help="directory containing tokenizer")
+    parser.add_argument("--dataset_dir", help="directory contaning dataset")
+    parser.add_argument("--peft_config", help="peft_config")
+    parser.add_argument("--train_params", help="hugging face training parameters")
 
     return parser.parse_args()
 
+
 if __name__ == "__main__":
     args = parse_arguments()
     model, tokenizer = setup_model_and_tokenizer(args.token_dir, args.model_dir)