diff --git a/src/llmtuner/model/loader.py b/src/llmtuner/model/loader.py
index 2434016e..3df33c70 100644
--- a/src/llmtuner/model/loader.py
+++ b/src/llmtuner/model/loader.py
@@ -28,7 +28,7 @@ from llmtuner.extras.packages import is_flash_attn2_available
 from llmtuner.extras.patches import llama_patch as LlamaPatches
 from llmtuner.hparams import FinetuningArguments
 from llmtuner.model.adapter import init_adapter
-from llmtuner.model.utils import load_valuehead_params, prepare_model_for_training
+from llmtuner.model.utils import load_valuehead_params, prepare_model_for_training, resize_embedding_layer
 
 if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer
@@ -185,6 +185,9 @@ def load_model_and_tokenizer(
         **config_kwargs
     )
 
+    # Resize token embeddings
+    resize_embedding_layer(model, tokenizer)
+
     # Disable custom generate method (for Qwen and Baichuan2)
     if isinstance(model, PreTrainedModel) and "GenerationMixin" not in str(model.generate.__func__):
         model.generate = MethodType(PreTrainedModel.generate, model)
diff --git a/src/llmtuner/model/utils.py b/src/llmtuner/model/utils.py
index 42bef35b..b52582f5 100644
--- a/src/llmtuner/model/utils.py
+++ b/src/llmtuner/model/utils.py
@@ -11,6 +11,7 @@ from llmtuner.hparams import ModelArguments, FinetuningArguments
 
 if TYPE_CHECKING:
     from transformers.modeling_utils import PreTrainedModel
+    from transformers.tokenization_utils import PreTrainedTokenizer
     from llmtuner.hparams import DataArguments
 
 
@@ -181,3 +182,14 @@ def prepare_model_for_training(
             output_layer.register_forward_hook(fp32_forward_post_hook)
 
     return model
+
+
+def resize_embedding_layer(model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer") -> None:
+    r"""
+    Resize token embeddings.
+    """
+    old_vocab_size = model.get_input_embeddings().weight.size(0)
+    new_vocab_size = len(tokenizer)
+    if new_vocab_size != old_vocab_size:
+        model.resize_token_embeddings(new_vocab_size, pad_to_multiple_of=64)
+        logger.info("Resized embedding tokens from {} to {}.".format(old_vocab_size, new_vocab_size))