diff --git a/llm/predictor.py b/llm/predictor.py
index bbbaf1ceace4..5353d7f627ec 100644
--- a/llm/predictor.py
+++ b/llm/predictor.py
@@ -871,7 +871,7 @@ def create_predictor(
                 )
                 model.eval()
             else:
-                raise ValueError("the `model type` should be one of [llama, chatglm, bloom, gpt]")
+                raise ValueError("the `model type` should be one of [llama, chatglm, bloom, gpt, qwen]")
             predictor = DygraphInferencePredictor(predictor_args, model=model, tokenizer=tokenizer)
         elif predictor_args.mode == "static":
             config = AutoConfig.from_pretrained(predictor_args.model_name_or_path)
@@ -915,8 +915,16 @@ def create_predictor(
                 cache_kvs_shape = GPTForCausalLMInferenceModel.get_cache_kvs_shape(
                     config, predictor_args.batch_size, predictor_args.total_max_length
                 )
+            elif "qwen" in config.architectures[0].lower():
+                from paddlenlp.experimental.transformers import (
+                    QWenForCausalLMInferenceModel,
+                )
+
+                cache_kvs_shape = QWenForCausalLMInferenceModel.get_cache_kvs_shape(
+                    config, predictor_args.batch_size, predictor_args.total_max_length
+                )
             else:
-                raise ValueError("the `model type` should be one of [llama, chatglm, bloom, gpt]")
+                raise ValueError("the `model type` should be one of [llama, chatglm, bloom, gpt, qwen]")
             predictor = StaticInferencePredictor(predictor_args, cache_kvs_shape, tokenizer=tokenizer)
         else:
             raise ValueError("the `mode` should be one of [dynamic, static]")