diff --git a/examples/conversation_with_agent_with_finetuned_model/FinetuneDialogAgent.py b/examples/conversation_with_agent_with_finetuned_model/FinetuneDialogAgent.py
index f3167fde4..e03fa2423 100644
--- a/examples/conversation_with_agent_with_finetuned_model/FinetuneDialogAgent.py
+++ b/examples/conversation_with_agent_with_finetuned_model/FinetuneDialogAgent.py
@@ -43,7 +43,6 @@ def __init__(
         Note:
             Refer to `class DialogAgent(AgentBase)` for more information.
         """
-        # pylint: disable=useless-parent-delegation
         super().__init__(
             name,
             sys_prompt,
@@ -51,6 +50,7 @@ def __init__(
             use_memory,
             memory_config,
         )
+        self.finetune = True
 
     def load_model(
         self,
diff --git a/examples/conversation_with_agent_with_finetuned_model/conversation_with_agent_with_finetuned_model.py b/examples/conversation_with_agent_with_finetuned_model/conversation_with_agent_with_finetuned_model.py
index 7e297aee9..292694905 100644
--- a/examples/conversation_with_agent_with_finetuned_model/conversation_with_agent_with_finetuned_model.py
+++ b/examples/conversation_with_agent_with_finetuned_model/conversation_with_agent_with_finetuned_model.py
@@ -9,7 +9,10 @@
 Features include model and tokenizer loading,
 and fine-tuning on the lima dataset with adjustable parameters.
 """
-
+# This import is necessary for AgentScope to properly use
+# HuggingFaceWrapper even though it's not explicitly used in this file.
+# To remove the pylint disable without causing issues
+# HuggingFaceWrapper needs to be put under src/agentscope/agents.
 # pylint: disable=unused-import
 from huggingface_model import HuggingFaceWrapper
 from FinetuneDialogAgent import FinetuneDialogAgent
@@ -52,6 +55,7 @@ def main() -> None:
                 # loading a model saved as lora model
                 "fine_tune_config": {
                     "continue_lora_finetuning": False,
+                    "max_seq_length": 4096,
                     "lora_config": {
                         "r": 16,
                         "lora_alpha": 32,
diff --git a/examples/conversation_with_agent_with_finetuned_model/huggingface_model.py b/examples/conversation_with_agent_with_finetuned_model/huggingface_model.py
index d8bf7791d..9e0731e25 100644
--- a/examples/conversation_with_agent_with_finetuned_model/huggingface_model.py
+++ b/examples/conversation_with_agent_with_finetuned_model/huggingface_model.py
@@ -80,10 +80,13 @@ def __init__(
                                                fine-tuning the model.
             **kwargs: Additional keyword arguments.
         """
-        super().__init__(config_name=config_name)
+        super().__init__(
+            config_name=config_name,
+            model_name=pretrained_model_name_or_path,
+        )
         self.model = None
         self.tokenizer = None
-        self.max_length = max_length  # Set max_length as an attribute
+        self.max_length = max_length
         self.pretrained_model_name_or_path = pretrained_model_name_or_path
         self.local_model_path = local_model_path
         self.lora_config = None
@@ -358,6 +361,8 @@ def load_tokenizer(
                     f" from '{local_model_path}'",
                 )
             self.tokenizer.padding_side = "right"
+            if self.tokenizer.pad_token is None:
+                self.tokenizer.pad_token = self.tokenizer.eos_token
 
         except Exception as e:
             # Handle exceptions during model loading,
@@ -666,10 +671,13 @@ def fine_tune_training(
             "optim": "paged_adamw_8bit",
             "logging_steps": 1,
         }
+        max_seq_length_default = 4096
 
         lora_config_default = {}
 
         if fine_tune_config is not None:
+            if fine_tune_config.get("max_seq_length") is not None:
+                max_seq_length_default = fine_tune_config["max_seq_length"]
             if fine_tune_config.get("training_args") is not None:
                 training_defaults.update(fine_tune_config["training_args"])
             if fine_tune_config.get("lora_config") is not None:
@@ -709,7 +717,7 @@ def fine_tune_training(
                 else {}
             ),
             args=trainer_args,
-            max_seq_length=4096,
+            max_seq_length=max_seq_length_default,
         )
 
         logger.info(
@@ -718,14 +726,7 @@ def fine_tune_training(
             timestamp=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
         )
 
-        try:
-            trainer.train()
-        except Exception as e:
-            import traceback
-
-            logger.error(f"Error during training: {e}")
-            traceback.print_exc()
-            raise
+        trainer.train()
 
         now = datetime.now()
         time_string = now.strftime("%Y-%m-%d_%H-%M-%S")