diff --git a/python/llm/src/ipex_llm/transformers/npu_model.py b/python/llm/src/ipex_llm/transformers/npu_model.py
index 65390aea..15605d07 100644
--- a/python/llm/src/ipex_llm/transformers/npu_model.py
+++ b/python/llm/src/ipex_llm/transformers/npu_model.py
@@ -423,23 +423,36 @@ class _BaseAutoModelClass:
 
         if enable_cpp_backend:
             from .npu_models.npu_llm_cpp import load_model_from_file
-            from .npu_models.convert import generate
-            dummy_model = torch.nn.Module()
+            from .npu_models.convert import generate, general_convert
+            from .npu_models.convert import prepare_input_ids, causal_lm_forward
+            config = AutoConfig.from_pretrained(
+                os.path.join(pretrained_model_name_or_path, "config.json"),
+                trust_remote_code=trust_remote_code)
+            with torch.device('meta'):
+                model = transformers.AutoModelForCausalLM.from_config(
+                    config, trust_remote_code=trust_remote_code)
             try:
                 model_ptr = load_model_from_file(pretrained_model_name_or_path)
-                dummy_model.config = PretrainedConfig.from_dict(config_dict)
-                dummy_model.model_ptr = model_ptr
-                dummy_model.save_directory = pretrained_model_name_or_path
-                dummy_model.kv_len = config_dict['kv_len']
-                dummy_model.vocab_size = config_dict['vocab_size']
+                model.config = config
+                model.model_ptr = model_ptr
+                model.save_directory = pretrained_model_name_or_path
+                model.kv_len = config_dict['kv_len']
+                model.vocab_size = config_dict['vocab_size']
+                model.logits_buffer = torch.empty(1, 1, model.vocab_size, dtype=torch.float32)
             except:
                 invalidInputError(False,
-                                  "False to InitLLMPipeline.")
-            dummy_model.eval()
+                                  "Fail to InitLLMPipeline.")
+            model.eval()
+            # patch model forward
+            from transformers.modeling_utils import PreTrainedModel
+            general_convert(model, PreTrainedModel, prepare_input_ids,
+                            "prepare_inputs_for_generation")
+            general_convert(model, PreTrainedModel, causal_lm_forward)
             # patch generate function
             import types
-            dummy_model.generate = types.MethodType(generate, dummy_model)
-            return dummy_model
+            model.original_generate = model.generate
+            model.generate = types.MethodType(generate, model)
+            return model
 
         has_remote_code = hasattr(config, "auto_map") and cls.HF_Model.__name__ in config.auto_map
         has_local_code = type(config) in cls.HF_Model._model_mapping.keys()