diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen-vl/README.md b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen-vl/README.md
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diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen-vl/chat.py b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen-vl/chat.py
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+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from bigdl.llm.transformers import AutoModel, AutoModelForCausalLM
+from transformers import AutoTokenizer, LlamaTokenizer
+from transformers.generation import GenerationConfig
+import torch
+import time
+import os
+import argparse
+from bigdl.llm import optimize_model
+import intel_extension_for_pytorch as ipex
+
+torch.manual_seed(1234)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for Qwen-VL model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen-VL-Chat",
+                        help='The huggingface repo id for the Qwen-VL model to be downloaded'
+                             ', or the path to the huggingface checkpoint folder')
+    parser.add_argument('--n-predict', type=int, default=32, help='Max tokens to predict')
+    
+    current_path = os.path.dirname(os.path.abspath(__file__))
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path  
+        
+    # Load model
+    # For successful BigDL-LLM optimization on Qwen-VL-Chat, skip the 'c_fc' and 'out_proj' modules during optimization
+    model = AutoModelForCausalLM.from_pretrained(model_path, 
+                                                 load_in_4bit=True, 
+                                                 trust_remote_code=True, 
+                                                 modules_to_not_convert=['c_fc', 'out_proj'])
+    model = model.to('xpu')
+
+    # Specify hyperparameters for generation (No need to do this if you are using transformers>=4.32.0)
+    model.generation_config = GenerationConfig.from_pretrained(model_path, trust_remote_code=True)
+    
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+    # Session ID
+    session_id = 1
+
+    while True:
+      print('-'*20, 'Session %d' % session_id, '-'*20)
+      image_input = input(f' Please input a picture: ')
+      if image_input.lower() == 'exit' : # type 'exit' to quit the dialouge
+         break
+
+      text_input = input(f' Please enter the text: ')
+      if text_input.lower() == 'exit' : # type 'exit' to quit the dialouge
+         break
+      
+      if session_id == 1:
+         history = None
+
+      all_input = [{'image': image_input}, {'text': text_input}]
+      input_list = [_input for _input in all_input if list(_input.values())[0] != '']
+
+      if len(input_list) == 0:
+         print("Input list should not be empty. Please try again with valid input.")
+         continue
+      
+      query = tokenizer.from_list_format(input_list)
+      response, history = model.chat(tokenizer, query = query, history = history)
+      torch.xpu.synchronize()
+
+      print('-'*10, 'Response', '-'*10)
+      print(response, '\n')
+
+      image = tokenizer.draw_bbox_on_latest_picture(response, history)
+      if image is not None:
+         image.save(os.path.join(current_path, f'Session_{session_id}.png'), )
+
+      session_id += 1