diff --git a/python/llm/src/ipex_llm/transformers/convert.py b/python/llm/src/ipex_llm/transformers/convert.py
index 7edb2b7a..b8c68995 100644
--- a/python/llm/src/ipex_llm/transformers/convert.py
+++ b/python/llm/src/ipex_llm/transformers/convert.py
@@ -1755,19 +1755,29 @@ def _optimize_post(model, lightweight_bmm=False):
                         module.MiniCPMModel,
                         minicpm_model_forward)
     elif model.config.model_type == "minicpmv":
-        if model.config.hidden_size == 3584 and model.config.vocab_size == 151666:
-            model.llm.config.model_type = "qwen2"
-            _optimize_post(model.llm, lightweight_bmm=lightweight_bmm)
-            model.llm.config.model_type = "minicpmv"
         modeling_module_name = model.__class__.__module__
         module = importlib.import_module(modeling_module_name)
         from ipex_llm.transformers.models.minicpmv import minicpmv_generate_wrapper
         minicpmv_generate = minicpmv_generate_wrapper(module.MiniCPMV.generate)
         model.generate = MethodType(minicpmv_generate, model)
 
-        modeling_module_name = model.vpm.__class__.__module__
-        module = importlib.import_module(modeling_module_name)
-        from ipex_llm.transformers.models.minicpmv import siglip_attention_forward
-        convert_forward(model, module.SiglipAttention, siglip_attention_forward)
+        if model.config.hidden_size == 3584 and model.config.vocab_size == 151666:
+            # MiniCPM-V 2.6
+            model.llm.config.model_type = "qwen2"
+            _optimize_post(model.llm, lightweight_bmm=lightweight_bmm)
+            model.llm.config.model_type = "minicpmv"
+        elif model.config.hidden_size == 4096 and model.config.vocab_size == 128256:
+            # MiniCPM-V 2.5
+            pass
+
+        vpm_modeling_module_name = model.vpm.__class__.__module__
+        vpm_module = importlib.import_module(vpm_modeling_module_name)
+        if model.vpm.config.model_type == "siglip":
+            # MiniCPM-V 2.6
+            from ipex_llm.transformers.models.minicpmv import siglip_attention_forward
+            convert_forward(model, vpm_module.SiglipAttention, siglip_attention_forward)
+        elif model.vpm.config.model_type == "idefics2":
+            # MiniCPM-V 2.5
+            pass
 
     return model
diff --git a/python/llm/src/ipex_llm/transformers/models/minicpmv.py b/python/llm/src/ipex_llm/transformers/models/minicpmv.py
index 159e61b6..03d8d2f4 100644
--- a/python/llm/src/ipex_llm/transformers/models/minicpmv.py
+++ b/python/llm/src/ipex_llm/transformers/models/minicpmv.py
@@ -69,32 +69,12 @@ def patched_repetition_penalty_call(self, input_ids: torch.LongTensor, scores: t
 
 def minicpmv_generate_wrapper(origin_generate):
     def generate(
-        self,
-        input_ids=None,
-        pixel_values=None,
-        tgt_sizes=None,
-        image_bound=None,
-        attention_mask=None,
-        tokenizer=None,
-        vision_hidden_states=None,
-        return_vision_hidden_states=False,
-        stream=False,
-        decode_text=False,
+        *inputs,
         **kwargs
     ):
         RepetitionPenaltyLogitsProcessor.__call__ = patched_repetition_penalty_call
         return origin_generate(
-            self=self,
-            input_ids=input_ids,
-            pixel_values=pixel_values,
-            tgt_sizes=tgt_sizes,
-            image_bound=image_bound,
-            attention_mask=attention_mask,
-            tokenizer=tokenizer,
-            vision_hidden_states=vision_hidden_states,
-            return_vision_hidden_states=return_vision_hidden_states,
-            stream=stream,
-            decode_text=decode_text,
-            **kwargs
+            *inputs,
+            **kwargs,
         )
     return generate