diff --git a/src/llmtuner/tuner/core/loader.py b/src/llmtuner/tuner/core/loader.py
index 8de98387..e480d44c 100644
--- a/src/llmtuner/tuner/core/loader.py
+++ b/src/llmtuner/tuner/core/loader.py
@@ -153,6 +153,10 @@ def load_model_and_tokenizer(
     if "GenerationMixin" not in str(model.generate.__func__):
         model.generate = MethodType(PreTrainedModel.generate, model)
 
+    # Fix LM head (for ChatGLM2)
+    if not hasattr(model, "lm_head"):
+        setattr(model, "lm_head", model.transformer.output_layer)
+
     # Register auto class to save the custom code files.
     if isinstance(config, PretrainedConfig) and "AutoConfig" in getattr(config, "auto_map", {}):
         config.__class__.register_for_auto_class()
diff --git a/src/llmtuner/tuner/dpo/trainer.py b/src/llmtuner/tuner/dpo/trainer.py
index f0da9eaa..458e99db 100644
--- a/src/llmtuner/tuner/dpo/trainer.py
+++ b/src/llmtuner/tuner/dpo/trainer.py
@@ -32,11 +32,11 @@ class DPOPeftTrainer(PeftModelMixin, DPOTrainer):
         self._stored_metrics = defaultdict(lambda: defaultdict(list))
 
         Trainer.__init__(self, **kwargs)
+        if not hasattr(self, "accelerator"):
+            raise AttributeError("Please update `transformers`.")
+
         if ref_model is not None:
-            if hasattr(self, "accelerator"):
-                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
-            else:
-                raise AttributeError("Please update `transformers`.")
+            self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
 
     def concatenated_forward(
         self,
diff --git a/src/llmtuner/webui/components/eval.py b/src/llmtuner/webui/components/eval.py
index 72d08a1f..48372b4c 100644
--- a/src/llmtuner/webui/components/eval.py
+++ b/src/llmtuner/webui/components/eval.py
@@ -45,7 +45,7 @@ def create_eval_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict
     with gr.Box():
         output_box = gr.Markdown()
 
-    input_list = [
+    input_components = [
         top_elems["lang"],
         top_elems["model_name"],
         top_elems["checkpoints"],
@@ -62,13 +62,13 @@ def create_eval_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict
         predict
     ]
 
-    output_list = [
+    output_components = [
         output_box,
         process_bar
     ]
 
-    cmd_preview_btn.click(runner.preview_eval, input_list, output_list)
-    start_btn.click(runner.run_eval, input_list, output_list)
+    cmd_preview_btn.click(runner.preview_eval, input_components, output_components)
+    start_btn.click(runner.run_eval, input_components, output_components)
     stop_btn.click(runner.set_abort, queue=False)
 
     return dict(