diff --git a/tests/kit/model_zoo/transformers/__init__.py b/tests/kit/model_zoo/transformers/__init__.py
index be6d92f01..ac15a4bdb 100644
--- a/tests/kit/model_zoo/transformers/__init__.py
+++ b/tests/kit/model_zoo/transformers/__init__.py
@@ -8,6 +8,7 @@ from .gpt import *
 from .gptj import *
 from .llama import *
 from .opt import *
+from .qwen2 import *
 from .sam import *
 from .t5 import *
 from .vit import *
diff --git a/tests/kit/model_zoo/transformers/qwen2.py b/tests/kit/model_zoo/transformers/qwen2.py
index 3110d8e53..e69de29bb 100644
--- a/tests/kit/model_zoo/transformers/qwen2.py
+++ b/tests/kit/model_zoo/transformers/qwen2.py
@@ -1,87 +0,0 @@
-import torch
-import transformers
-
-from ..registry import ModelAttribute, model_zoo
-
-try:
-    from transformers import Qwen2Config
-
-    HAS_QWEN2 = True
-except ImportError:
-    HAS_QWEN2 = False
-
-if HAS_QWEN2:
-    # ===============================
-    # Register Qwen2
-    # ===============================
-
-    def data_gen():
-        # the input ids are corresponding to the sentence
-        # 'Hello, my dog is cute'
-        #
-        # the code is give below:
-        # -----------------------------------
-        # from transformers import Qwen2TokenizerFast
-        # tokenizer = Qwen2TokenizerFast.from_pretrained("Qwen/Qwen1.5-7B-Chat")
-        # input = 'Hello, my dog is cute'
-        # tokenized_input = tokenizer(input, return_tensors='pt').to('cuda')
-        # -----------------------------------
-
-        input_ids = torch.Tensor([[9707, 11, 847, 5562, 374, 18838], [9707, 11, 847, 5562, 374, 18838]]).long()
-        attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]]).long()
-        return dict(input_ids=input_ids, attention_mask=attention_mask)
-
-    # label is needed for casual lm
-    def data_gen_for_casual_lm():
-        data = data_gen()
-        labels = data["input_ids"].clone()
-        data["labels"] = labels
-        return data
-
-    # transform the output to a dict
-    output_transform_fn = lambda x: x
-
-    # function to get the loss
-    loss_fn = lambda output: output["last_hidden_state"].mean()
-    loss_fn_for_casual_lm = lambda output: output["loss"]
-    loss_fn_for_seq_classification = lambda output: output["logits"].mean()
-
-    config = Qwen2Config(
-        hidden_size=128,
-        intermediate_size=256,
-        max_window_layers=4,
-        num_attention_heads=16,
-        num_hidden_layers=4,
-        num_key_value_heads=16,
-    )
-
-    config.pad_token_id = 0
-
-    # register the following models
-    # transformers.Qwen2Model,
-    # transformers.Qwen2ForCausalLM,
-    # transformers.Qwen2ForSequenceClassification,
-    model_zoo.register(
-        name="transformers_qwen2",
-        model_fn=lambda: transformers.Qwen2Model(config),
-        data_gen_fn=data_gen,
-        output_transform_fn=output_transform_fn,
-        loss_fn=loss_fn,
-        model_attribute=ModelAttribute(has_control_flow=True),
-    )
-    model_zoo.register(
-        name="transformers_qwen2_for_casual_lm",
-        model_fn=lambda: transformers.Qwen2ForCausalLM(config),
-        data_gen_fn=data_gen_for_casual_lm,
-        output_transform_fn=output_transform_fn,
-        loss_fn=loss_fn_for_casual_lm,
-        model_attribute=ModelAttribute(has_control_flow=True),
-    )
-    model_zoo.register(
-        name="transformers_qwen2_for_sequence_classification",
-        model_fn=lambda: transformers.Qwen2ForSequenceClassification(config),
-        data_gen_fn=data_gen,
-        output_transform_fn=output_transform_fn,
-        loss_fn=loss_fn_for_seq_classification,
-        model_attribute=ModelAttribute(has_control_flow=True),
-    )