diff --git a/python/llm/example/transformers/transformers_int4/chatglm2/README.md b/python/llm/example/transformers/transformers_int4/chatglm2/README.md
index d005ecc2..ff9b051b 100644
--- a/python/llm/example/transformers/transformers_int4/chatglm2/README.md
+++ b/python/llm/example/transformers/transformers_int4/chatglm2/README.md
@@ -5,7 +5,7 @@ In this directory, you will find examples on how you could apply BigDL-LLM INT4
 ## 0. Requirements
 To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
 
-## Example: Predict Tokens using `generate()` API
+## Example 1: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM2 model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
 ### 1. Install
 We suggest using conda to manage environment:
@@ -74,3 +74,55 @@ Inference time: xxxx s
 
 答： Artificial Intelligence (AI) refers to the ability of a computer or machine to perform tasks that typically require human-like intelligence, such as understanding language, recognizing patterns
 ```
+
+## Example 2: Stream Chat using `stream_chat()` API
+In the example [streamchat.py](./streamchat.py), we show a basic use case for a ChatGLM2 model to stream chat, with BigDL-LLM INT4 optimizations.
+### 1. Install
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.9
+conda activate llm
+
+pip install bigdl-llm[all] # install bigdl-llm with 'all' option
+```
+
+### 2. Run
+**Stream Chat using `stream_chat()` API**:
+```
+python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION
+```
+
+**Chat using `chat()` API**:
+```
+python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION --disable-stream
+```
+
+Arguments info:
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the ChatGLM2 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/chatglm2-6b'`.
+- `--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.
+
+> **Note**: When loading the model in 4-bit, BigDL-LLM converts linear layers in the model into INT4 format. In theory, a *X*B model saved in 16-bit will requires approximately 2*X* GB of memory for loading, and ~0.5*X* GB memory for further inference.
+>
+> Please select the appropriate size of the ChatGLM2 model based on the capabilities of your machine.
+
+#### 2.1 Client
+On client Windows machine, it is recommended to run directly with full utilization of all cores:
+```powershell
+$env:PYTHONUNBUFFERED=1  # ensure stdout and stderr streams are sent straight to terminal without being first buffered
+python ./streamchat.py
+```
+
+#### 2.2 Server
+For optimal performance on server, it is recommended to set several environment variables (refer to [here](../README.md#best-known-configuration-on-linux) for more information), and run the example with all the physical cores of a single socket.
+
+E.g. on Linux,
+```bash
+# set BigDL-Nano env variables
+source bigdl-nano-init
+
+# e.g. for a server with 48 cores per socket
+export OMP_NUM_THREADS=48
+export PYTHONUNBUFFERED=1  # ensure stdout and stderr streams are sent straight to terminal without being first buffered
+numactl -C 0-47 -m 0 python ./streamchat.py
+```
diff --git a/python/llm/example/transformers/transformers_int4/chatglm2/streamchat.py b/python/llm/example/transformers/transformers_int4/chatglm2/streamchat.py
new file mode 100644
index 00000000..3bbf5333
--- /dev/null
+++ b/python/llm/example/transformers/transformers_int4/chatglm2/streamchat.py
@@ -0,0 +1,62 @@
+#
+# 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.
+#
+
+import torch
+import time
+import argparse
+import numpy as np
+
+from bigdl.llm.transformers import AutoModel
+from transformers import AutoTokenizer
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Stream Chat for ChatGLM2 model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="THUDM/chatglm2-6b",
+                        help='The huggingface repo id for the ChatGLM2 model to be downloaded'
+                             ', or the path to the huggingface 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')
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+    disable_stream = args.disable_stream
+
+    # Load model in 4 bit,
+    # which convert the relevant layers in the model into INT4 format
+    model = AutoModel.from_pretrained(model_path,
+                                      load_in_4bit=True,
+                                      trust_remote_code=True)
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                              trust_remote_code=True)
+
+    with torch.inference_mode():
+        if disable_stream:
+            # Chat
+            response, history = model.chat(tokenizer, args.question, history=[])
+            print('-'*20, 'Chat Output', '-'*20)
+            print(response)
+        else:
+            # Stream chat
+            response_ = ""
+            print('-'*20, 'Stream Chat Output', '-'*20)
+            for response, history in model.stream_chat(tokenizer, args.question, history=[]):
+                print(response.replace(response_, ""), end="")
+                response_ = response