diff --git a/python/llm/example/transformers/transformers_int4/chatglm/README.md b/python/llm/example/transformers/transformers_int4/chatglm/README.md
index e15f24b0..861ee12a 100644
--- a/python/llm/example/transformers/transformers_int4/chatglm/README.md
+++ b/python/llm/example/transformers/transformers_int4/chatglm/README.md
@@ -4,7 +4,7 @@ In this directory, you will find examples on how you could apply BigDL-LLM INT4
 
 > **Note**: If you want to download the Hugging Face *Transformers* model, please refer to [here](https://huggingface.co/docs/hub/models-downloading#using-git).
 >
-> BigDL-LLM optimizes the *Transformers* model in INT4 precision at runtime, so that no explicit conversion is needed.
+> BigDL-LLM optimizes the *Transformers* model in INT4 precision at runtime, and thus no explicit conversion is needed.
 
 ## 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.
@@ -19,30 +19,22 @@ After installing conda, create a Python environment for BigDL-LLM:
 conda create -n llm python=3.9 # recommend to use Python 3.9
 conda activate llm
 
-pip install bigdl-llm[all] # install bigdl-llm with 'all' option
+pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option
 ```
 
 ### 2. Run
-```
-python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
-```
-
-Arguments info:
-- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the ChatGLM model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/chatglm-6b'`.
-- `--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`.
-
-The expected output can be found in [Sample Output](#23-sample-output) section.
+After setting up the Python environment, you could run the example by following steps.
 
 > **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 ChatGLM 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:
+On client Windows machines, it is recommended to run directly with full utilization of all cores:
 ```powershell
-python ./generate.py 
+python ./generate.py --prompt 'AI是什么？'
 ```
+More information about arguments can be found in [Arguments Info](#23-arguments-info) section. The expected output can be found in [Sample Output](#24-sample-output) section.
 
 #### 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.
@@ -54,27 +46,22 @@ source bigdl-nano-init
 
 # e.g. for a server with 48 cores per socket
 export OMP_NUM_THREADS=48
-numactl -C 0-47 -m 0 python ./generate.py
+numactl -C 0-47 -m 0 python ./generate.py --prompt 'AI是什么？'
 ```
+More information about arguments can be found in [Arguments Info](#23-arguments-info) section. The expected output can be found in [Sample Output](#24-sample-output) section.
 
-#### 2.3 Sample Output
+#### 2.3 Arguments Info
+In the example, several arguments can be passed to satisfy your requirements:
+
+- `--repo-id-or-model-path`: str, argument defining the huggingface repo id for the ChatGLM model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/chatglm-6b'`.
+- `--prompt`: str, argument defining the prompt to be inferred (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
+- `--n-predict`: int, argument defining the max number of tokens to predict. It is default to be `32`.
+
+#### 2.4 Sample Output
 #### [THUDM/chatglm-6b](https://huggingface.co/THUDM/chatglm-6b)
 ```log
 Inference time: xxxx s
--------------------- Prompt --------------------
-问：AI是什么？
-答：
 -------------------- Output --------------------
 问:AI是什么?
 答: AI是人工智能(Artificial Intelligence)的缩写,指的是一种能够模拟人类智能的技术或系统。AI系统可以通过学习、推理、解决问题等方式,实现类似于
 ```
-
-```log
-Inference time: xxxx s
--------------------- Prompt --------------------
-问：What is AI?
-答：
--------------------- Output --------------------
-问:What is AI?
-答: AI stands for "Artificial Intelligence." AI refers to the development of computer systems that can perform tasks that typically require human intelligence, such as recognizing speech, understanding natural
-```
diff --git a/python/llm/example/transformers/transformers_int4/chatglm/generate.py b/python/llm/example/transformers/transformers_int4/chatglm/generate.py
index 7a5e604d..b5f5ab6e 100644
--- a/python/llm/example/transformers/transformers_int4/chatglm/generate.py
+++ b/python/llm/example/transformers/transformers_int4/chatglm/generate.py
@@ -63,7 +63,5 @@ if __name__ == '__main__':
         end = time.time()
         output_str = tokenizer.decode(output[0], skip_special_tokens=True)
         print(f'Inference time: {end-st} s')
-        print('-'*20, 'Prompt', '-'*20)
-        print(prompt)
         print('-'*20, 'Output', '-'*20)
         print(output_str)