diff --git a/src/llamafactory/model/adapter.py b/src/llamafactory/model/adapter.py
index 8b8c6fb8..9a000f41 100644
--- a/src/llamafactory/model/adapter.py
+++ b/src/llamafactory/model/adapter.py
@@ -188,7 +188,7 @@ def _setup_lora_tuning(
 
         if adapter_to_resume is not None:  # resume lora training
             if model_args.use_unsloth:
-                model = load_unsloth_peft_model(config, model_args, is_trainable=is_trainable)
+                model = load_unsloth_peft_model(config, model_args, finetuning_args, is_trainable=is_trainable)
             else:
                 model = PeftModel.from_pretrained(model, adapter_to_resume, is_trainable=is_trainable, **init_kwargs)
 
diff --git a/src/llamafactory/model/model_utils/unsloth.py b/src/llamafactory/model/model_utils/unsloth.py
index 37791524..7792857a 100644
--- a/src/llamafactory/model/model_utils/unsloth.py
+++ b/src/llamafactory/model/model_utils/unsloth.py
@@ -80,12 +80,12 @@ def get_unsloth_peft_model(
 
 
 def load_unsloth_peft_model(
-    config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool
+    config: "PretrainedConfig", model_args: "ModelArguments", finetuning_args: "FinetuningArguments", is_trainable: bool
 ) -> "PreTrainedModel":
     r"""Load peft model with unsloth. Used in both training and inference."""
     from unsloth import FastLanguageModel  # type: ignore
 
-    unsloth_kwargs = _get_unsloth_kwargs(config, model_args.adapter_name_or_path[0], model_args)
+    unsloth_kwargs = _get_unsloth_kwargs(config, model_args.adapter_name_or_path[0], model_args, finetuning_args)
     try:
         if not is_trainable:
             unsloth_kwargs["use_gradient_checkpointing"] = False