diff --git a/python/llm/example/GPU/HuggingFace/LLM/chatglm3/README.md b/python/llm/example/GPU/HuggingFace/LLM/chatglm3/README.md
index 111c628c526..7a018f44e65 100644
--- a/python/llm/example/GPU/HuggingFace/LLM/chatglm3/README.md
+++ b/python/llm/example/GPU/HuggingFace/LLM/chatglm3/README.md
@@ -1,6 +1,6 @@
 # ChatGLM3
 
-In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on ChatGLM3 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b) as a reference ChatGLM3 model.
+In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on ChatGLM3 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b) (or [ZhipuAI/chatglm3-6b](https://www.modelscope.cn/models/ZhipuAI/chatglm3-6b) for ModelScope) as a reference ChatGLM3 model.
 
 ## 0. Requirements
 To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
@@ -13,6 +13,9 @@ conda create -n llm python=3.11
 conda activate llm
 # below command will install intel_extension_for_pytorch==2.1.10+xpu as default
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
+
+# [optional] only needed if you would like to use ModelScope as model hub
+pip install modelscope==1.11.0
 ```
 
 ### 1.2 Installation on Windows
@@ -23,6 +26,9 @@ conda activate llm
 
 # below command will install intel_extension_for_pytorch==2.1.10+xpu as default
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
+
+# [optional] only needed if you would like to use ModelScope as model hub
+pip install modelscope==1.11.0
 ```
 
 ## 2. Configures OneAPI environment variables for Linux
@@ -93,14 +99,19 @@ set SYCL_CACHE_PERSISTENT=1
 ### Example 1: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM3 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
 
-```
+```bash
+# for Hugging Face model hub
 python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
+
+# for ModelScope model hub
+python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT --modelscope
 ```
 
 Arguments info:
-- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the ChatGLM3 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/chatglm3-6b'`.
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the **Hugging Face** or **ModelScope** repo id for the ChatGLM3 model to be downloaded, or the path to the checkpoint folder. It is default to be `'THUDM/chatglm3-6b'` for **Hugging Face** or `ZhipuAI/chatglm3-6b` for **ModelScope**.
 - `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
 - `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
+- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**.
 
 #### Sample Output
 #### [THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b)
@@ -133,16 +144,25 @@ AI stands for Artificial Intelligence. It refers to the development of computer
 In the example [streamchat.py](./streamchat.py), we show a basic use case for a ChatGLM3 model to stream chat, with IPEX-LLM INT4 optimizations.
 
 **Stream Chat using `stream_chat()` API**:
-```
+```bash
+# for Hugging Face model hub
 python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION
+
+# for ModelScope model hub
+python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION --modelscope
 ```
 
 **Chat using `chat()` API**:
-```
+```bash
+# for Hugging Face model hub
 python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION --disable-stream
+
+# for ModelScope model hub
+python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION --disable-stream --modelscope
 ```
 
 Arguments info:
-- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the ChatGLM3 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/chatglm3-6b'`.
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the **Hugging Face** or **ModelScope** repo id for the ChatGLM3 model to be downloaded, or the path to the checkpoint folder. It is default to be `'THUDM/chatglm3-6b'` for **Hugging Face** or `ZhipuAI/chatglm3-6b` for **ModelScope**.
 - `--question QUESTION`: argument defining the question to ask. It is default to be `"晚上睡不着应该怎么办"`.
 - `--disable-stream`: argument defining whether to stream chat. If include `--disable-stream` when running the script, the stream chat is disabled and `chat()` API is used.
+- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**.
diff --git a/python/llm/example/GPU/HuggingFace/LLM/chatglm3/generate.py b/python/llm/example/GPU/HuggingFace/LLM/chatglm3/generate.py
index da730d70cfc..ee5e77b10eb 100644
--- a/python/llm/example/GPU/HuggingFace/LLM/chatglm3/generate.py
+++ b/python/llm/example/GPU/HuggingFace/LLM/chatglm3/generate.py
@@ -20,7 +20,6 @@
 import numpy as np
 
 from ipex_llm.transformers import AutoModel
-from transformers import AutoTokenizer
 
 # you could tune the prompt based on your own model,
 # here the prompt tuning refers to https://github.com/THUDM/ChatGLM3/blob/main/PROMPT.md
@@ -28,16 +27,27 @@
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for ChatGLM3 model')
-    parser.add_argument('--repo-id-or-model-path', type=str, default="THUDM/chatglm3-6b",
-                        help='The huggingface repo id for the ChatGLM3 model to be downloaded'
-                             ', or the path to the huggingface checkpoint folder')
+    parser.add_argument('--repo-id-or-model-path', type=str, 
+                        help='The Hugging Face or ModelScope repo id for the ChatGLM3 model to be downloaded'
+                             ', or the path to the checkpoint folder')
     parser.add_argument('--prompt', type=str, default="AI是什么？",
                         help='Prompt to infer')
     parser.add_argument('--n-predict', type=int, default=32,
                         help='Max tokens to predict')
+    parser.add_argument('--modelscope', action="store_true", default=False, 
+                        help="Use models from modelscope")
 
     args = parser.parse_args()
-    model_path = args.repo_id_or_model_path
+
+    if args.modelscope:
+        from modelscope import AutoTokenizer
+        model_hub = 'modelscope'
+    else:
+        from transformers import AutoTokenizer
+        model_hub = 'huggingface'
+    
+    model_path = args.repo_id_or_model_path if args.repo_id_or_model_path else \
+        ("ZhipuAI/chatglm3-6b" if args.modelscope else "THUDM/chatglm3-6b")
 
     # Load model in 4 bit,
     # which convert the relevant layers in the model into INT4 format
@@ -47,7 +57,8 @@
                                       load_in_4bit=True,
                                       optimize_model=True,
                                       trust_remote_code=True,
-                                      use_cache=True)
+                                      use_cache=True,
+                                      model_hub=model_hub)
     model = model.half().to('xpu')
 
     # Load tokenizer
diff --git a/python/llm/example/GPU/HuggingFace/LLM/chatglm3/streamchat.py b/python/llm/example/GPU/HuggingFace/LLM/chatglm3/streamchat.py
index 986a7365b65..305e3c051f7 100644
--- a/python/llm/example/GPU/HuggingFace/LLM/chatglm3/streamchat.py
+++ b/python/llm/example/GPU/HuggingFace/LLM/chatglm3/streamchat.py
@@ -20,21 +20,32 @@
 import numpy as np
 
 from ipex_llm.transformers import AutoModel
-from transformers import AutoTokenizer
 
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Stream Chat for ChatGLM3 model')
-    parser.add_argument('--repo-id-or-model-path', type=str, default="THUDM/chatglm3-6b",
-                        help='The huggingface repo id for the ChatGLM3 model to be downloaded'
-                             ', or the path to the huggingface checkpoint folder')
+    parser.add_argument('--repo-id-or-model-path', type=str,
+                        help='The Hugging Face or ModelScope repo id for the ChatGLM3 model to be downloaded'
+                             ', or the path to the checkpoint folder')
     parser.add_argument('--question', type=str, default="晚上睡不着应该怎么办",
                         help='Qustion you want to ask')
     parser.add_argument('--disable-stream', action="store_true",
                         help='Disable stream chat')
+    parser.add_argument('--modelscope', action="store_true", default=False, 
+                        help="Use models from modelscope")
 
     args = parser.parse_args()
-    model_path = args.repo_id_or_model_path
+    
+    if args.modelscope:
+        from modelscope import AutoTokenizer
+        model_hub = 'modelscope'
+    else:
+        from transformers import AutoTokenizer
+        model_hub = 'huggingface'
+    
+    model_path = args.repo_id_or_model_path if args.repo_id_or_model_path else \
+        ("ZhipuAI/chatglm3-6b" if args.modelscope else "THUDM/chatglm3-6b")
+
     disable_stream = args.disable_stream
 
     # Load model in 4 bit,
@@ -44,8 +55,9 @@
     model = AutoModel.from_pretrained(model_path,
                                       load_in_4bit=True,
                                       trust_remote_code=True,
-                                      optimize_model=True)
-    model.to('xpu')
+                                      optimize_model=True,
+                                      model_hub=model_hub)
+    model = model.half().to('xpu')
 
     # Load tokenizer
     tokenizer = AutoTokenizer.from_pretrained(model_path,