diff --git a/src/llamafactory/model/loader.py b/src/llamafactory/model/loader.py
index d327eecf..cbcc6b28 100644
--- a/src/llamafactory/model/loader.py
+++ b/src/llamafactory/model/loader.py
@@ -138,7 +138,7 @@ def load_model(
         if model_args.adapter_name_or_path is not None:
             lazy_load = True
         elif is_trainable:
-            model = load_unsloth_pretrained_model(config, model_args)
+            model = load_unsloth_pretrained_model(config, model_args, finetuning_args)
 
     if model is None and not lazy_load:
         init_kwargs["config"] = config
diff --git a/src/llamafactory/model/model_utils/unsloth.py b/src/llamafactory/model/model_utils/unsloth.py
index 8bb6aa64..352ef048 100644
--- a/src/llamafactory/model/model_utils/unsloth.py
+++ b/src/llamafactory/model/model_utils/unsloth.py
@@ -21,14 +21,14 @@ from ...extras.misc import get_current_device
 if TYPE_CHECKING:
     from transformers import PretrainedConfig, PreTrainedModel
 
-    from ...hparams import ModelArguments
+    from ...hparams import ModelArguments, FinetuningArguments
 
 
 logger = logging.get_logger(__name__)
 
 
 def _get_unsloth_kwargs(
-    config: "PretrainedConfig", model_name_or_path: str, model_args: "ModelArguments"
+    config: "PretrainedConfig", model_name_or_path: str, model_args: "ModelArguments", finetuning_args: "FinetuningArguments"
 ) -> dict[str, Any]:
     return {
         "model_name": model_name_or_path,
@@ -36,6 +36,7 @@ def _get_unsloth_kwargs(
         "dtype": model_args.compute_dtype,
         "load_in_4bit": model_args.quantization_bit == 4,
         "token": model_args.hf_hub_token,
+        "full_finetuning": finetuning_args.finetuning_type == "full",
         "device_map": {"": get_current_device()},
         "rope_scaling": getattr(config, "rope_scaling", None),
         "fix_tokenizer": False,
@@ -45,12 +46,12 @@ def _get_unsloth_kwargs(
 
 
 def load_unsloth_pretrained_model(
-    config: "PretrainedConfig", model_args: "ModelArguments"
+    config: "PretrainedConfig", model_args: "ModelArguments", finetuning_args: "FinetuningArguments"
 ) -> Optional["PreTrainedModel"]:
     r"""Optionally load pretrained model with unsloth. Used in training."""
     from unsloth import FastLanguageModel  # type: ignore
 
-    unsloth_kwargs = _get_unsloth_kwargs(config, model_args.model_name_or_path, model_args)
+    unsloth_kwargs = _get_unsloth_kwargs(config, model_args.model_name_or_path, model_args, finetuning_args)
     try:
         model, _ = FastLanguageModel.from_pretrained(**unsloth_kwargs)
     except NotImplementedError: