diff --git a/llmc/eval/eval_vlm.py b/llmc/eval/eval_vlm.py
index af519c61c..60e82aa5f 100644
--- a/llmc/eval/eval_vlm.py
+++ b/llmc/eval/eval_vlm.py
@@ -18,6 +18,7 @@ def __init__(self, config):
         ], 'VLM eval only support MME dataset now.'
         self.eval_dataset_path = self.eval_config['path']
         self.eval_bs = self.eval_config['bs']
+        self.output_include_input = True
         if self.dataset == 'MME':
             self.img_qas = self.load_mme()
         self.patch_datasets(config.model.type)
@@ -39,6 +40,10 @@ def patch_datasets(self, model_type):
                 for idx in range(len(self.img_qas)):
                     if '<image>\n' not in self.img_qas[idx]['question']:
                         self.img_qas[idx]['question'] = '<image>\n' + self.img_qas[idx]['question']
+        if model_type == 'InternVL2':
+            self.output_include_input = False
+        elif model_type == 'Llava':
+            self.output_include_input = True
 
     def eval(self, model, tokenizer):
         vlm_model = model.vlm_model
@@ -64,9 +69,14 @@ def eval(self, model, tokenizer):
                 for k, v in inputs.items()
             }
             outputs = vlm_model.generate(**inputs, max_new_tokens=32, do_sample=False)
-            gen_txts = vlm_tokenizer.batch_decode(
-                outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True
-            )
+            if self.output_include_input:
+                gen_txts = vlm_tokenizer.batch_decode(
+                    outputs[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True
+                )
+            else:
+                gen_txts = vlm_tokenizer.batch_decode(
+                    outputs, skip_special_tokens=True
+                )
             for n in range(len(batch_samples)):
                 result = batch_samples[n].copy()
                 result.update({'gen_txt': gen_txts[n]})