diff --git a/README.md b/README.md
index 80ccad31..26ff0793 100644
--- a/README.md
+++ b/README.md
@@ -186,6 +186,7 @@ Over 40 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLa
 | Bark | [link](python/llm/example/CPU/PyTorch-Models/Model/bark) | [link](python/llm/example/GPU/PyTorch-Models/Model/bark) |
 | SpeechT5 |  | [link](python/llm/example/GPU/PyTorch-Models/Model/speech-t5) |
 | Ziya-Coding-34B-v1.0 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/ziya) | |
+| Qwen1.5 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen1.5) |
 
 ***For more details, please refer to the `bigdl-llm` [Document](https://test-bigdl-llm.readthedocs.io/en/main/doc/LLM/index.html), [Readme](python/llm), [Tutorial](https://github.com/intel-analytics/bigdl-llm-tutorial) and [API Doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/LLM/index.html).***
 
diff --git a/python/llm/README.md b/python/llm/README.md
index 6e765d39..be0646a5 100644
--- a/python/llm/README.md
+++ b/python/llm/README.md
@@ -82,6 +82,8 @@ Over 20 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLa
 | Bark | [link](example/CPU/PyTorch-Models/Model/bark) | [link](example/GPU/PyTorch-Models/Model/bark) |
 | SpeechT5 |  | [link](example/GPU/PyTorch-Models/Model/speech-t5) |
 | Ziya-Coding-34B-v1.0 | [link](example/CPU/HF-Transformers-AutoModels/Model/ziya) | |
+| Qwen1.5 | [link](example/CPU/HF-Transformers-AutoModels/Model/qwen1.5) | [link](example/GPU/HF-Transformers-AutoModels/Model/qwen1.5) |
+
 
 ### Working with `bigdl-llm`
 
diff --git a/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5/README.md b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5/README.md
new file mode 100644
index 00000000..4f115651
--- /dev/null
+++ b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5/README.md
@@ -0,0 +1,87 @@
+# Qwen1.5
+
+In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Qwen1.5 models. For illustration purposes, we utilize the [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) as a reference Qwen1.5 model.
+
+## 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
+In the example [generate.py](./generate.py), we show a basic use case for a Qwen model to predict the next N tokens using `generate()` API, 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 --pre --upgrade bigdl-llm[all] # install bigdl-llm with 'all' option
+pip install transformers==4.37.0 # install the transformers which support Qwen2
+```
+
+### 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 Qwen model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'Qwen/Qwen1.5-7B-Chat'`.
+- `--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`.
+
+> **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 Qwen 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
+python ./generate.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-LLM env variables
+source bigdl-llm-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
+```
+
+#### 2.3 Sample Output
+#### [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat)
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+AI是什么？<|im_end|>
+<|im_start|>assistant
+-------------------- Output --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+AI是什么？<|im_end|>
+<|im_start|>assistant
+人工智能（AI）是指计算机科学的一个分支，旨在开发能够执行通常需要人类智能的任务的算法和系统。这些任务包括但不限于理解自然语言、
+```
+
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+What is AI?<|im_end|>
+<|im_start|>assistant
+-------------------- Output --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+What is AI?<|im_end|>
+<|im_start|>assistant
+AI, or artificial intelligence, refers to the simulation of human intelligence in machines that are designed to perform tasks that typically require human cognition, such as learning, reasoning
+```
\ No newline at end of file
diff --git a/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5/generate.py b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5/generate.py
new file mode 100644
index 00000000..becfb0cc
--- /dev/null
+++ b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5/generate.py
@@ -0,0 +1,77 @@
+#
+# 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 transformers import AutoTokenizer
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Qwen1.5-7B-Chat')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen1.5-7B-Chat",
+                        help='The huggingface repo id for the Qwen1.5 model to be downloaded'
+                             ', or the path to the huggingface 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')
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    
+    from bigdl.llm.transformers import AutoModelForCausalLM
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 load_in_4bit=True,
+                                                 trust_remote_code=True)
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                              trust_remote_code=True)
+    
+    prompt = args.prompt
+    
+    # Generate predicted tokens
+    with torch.inference_mode():
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+            ]
+        text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+            )
+        model_inputs = tokenizer([text], return_tensors="pt")
+        st = time.time()
+        generated_ids = model.generate(
+            model_inputs.input_ids,
+            max_new_tokens=args.n_predict
+            )
+        end = time.time()
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+            ]
+
+        response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Prompt', '-'*20)
+        print(prompt)
+        print('-'*20, 'Output', '-'*20)
+        print(response)
diff --git a/python/llm/example/CPU/PyTorch-Models/Model/qwen1.5/README.md b/python/llm/example/CPU/PyTorch-Models/Model/qwen1.5/README.md
new file mode 100644
index 00000000..2190be8d
--- /dev/null
+++ b/python/llm/example/CPU/PyTorch-Models/Model/qwen1.5/README.md
@@ -0,0 +1,86 @@
+# Qwen1.5
+In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate Qwen1.5 models. For illustration purposes, we utilize the [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) as reference Qwen1.5 model.
+
+## 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
+In the example [generate.py](./generate.py), we show a basic use case for a Qwen1.5 model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
+### 1. Install
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://docs.conda.io/en/latest/miniconda.html#).
+
+After installing conda, create a Python environment for BigDL-LLM:
+```bash
+conda create -n llm python=3.9 # recommend to use Python 3.9
+conda activate llm
+
+pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option
+pip install transformers==4.37.0 # install transformers which supports Qwen2
+```
+
+### 2. Run
+After setting up the Python environment, you could run the example by following steps.
+
+#### 2.1 Client
+On client Windows machines, it is recommended to run directly with full utilization of all cores:
+```powershell
+python ./generate.py --prompt 'What is AI?'
+```
+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.
+
+E.g. on Linux,
+```bash
+# set BigDL-LLM env variables
+source bigdl-llm-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 --prompt 'What is AI?'
+```
+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 Arguments Info
+In the example, several arguments can be passed to satisfy your requirements:
+
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Qwen1.5 to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'Qwen/Qwen1.5-7B-Chat'`.
+- `--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`.
+
+#### 2.3 Sample Output
+#### [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat)
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+AI是什么？<|im_end|>
+<|im_start|>assistant
+-------------------- Output --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+AI是什么？<|im_end|>
+<|im_start|>assistant
+AI（Artificial Intelligence）是指由计算机程序实现的智能，它使机器能够模拟人类的思考、学习和决策过程，从而解决各种复杂
+```
+
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+What is AI?<|im_end|>
+<|im_start|>assistant
+-------------------- Output --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+What is AI?<|im_end|>
+<|im_start|>assistant
+AI, or artificial intelligence, refers to the simulation of human intelligence in machines that are programmed to think, learn, and make decisions like humans. It involves the
+```
diff --git a/python/llm/example/CPU/PyTorch-Models/Model/qwen1.5/generate.py b/python/llm/example/CPU/PyTorch-Models/Model/qwen1.5/generate.py
new file mode 100644
index 00000000..da769fc3
--- /dev/null
+++ b/python/llm/example/CPU/PyTorch-Models/Model/qwen1.5/generate.py
@@ -0,0 +1,78 @@
+#
+# 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 transformers import AutoTokenizer
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Qwen1.5-7B-Chat')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen1.5-7B-Chat",
+                        help='The huggingface repo id for the Qwen1.5 model to be downloaded'
+                             ', or the path to the huggingface 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')
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    
+    from transformers import AutoModelForCausalLM
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 trust_remote_code=True)
+    
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                              trust_remote_code=True)
+    from bigdl.llm import optimize_model
+    model = optimize_model(model)
+    
+    prompt = args.prompt
+    # Generate predicted tokens
+    with torch.inference_mode():
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+            ]
+        text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+            )
+        model_inputs = tokenizer([text], return_tensors="pt")
+        st = time.time()
+        generated_ids = model.generate(
+            model_inputs.input_ids,
+            max_new_tokens=args.n_predict
+            )
+        end = time.time()
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+            ]
+
+        response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Prompt', '-'*20)
+        print(prompt)
+        print('-'*20, 'Output', '-'*20)
+        print(response)
diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen1.5/README.md b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen1.5/README.md
new file mode 100644
index 00000000..979c511a
--- /dev/null
+++ b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen1.5/README.md
@@ -0,0 +1,143 @@
+# Qwen1.5
+In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Qwen1.5 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) as a reference InternLM model.
+
+## 0. Requirements
+To run these examples with BigDL-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
+
+## Example: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a Qwen1.5 model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations on Intel GPUs.
+### 1. Install
+#### 1.1 Installation on Linux
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.9
+conda activate llm
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+pip install transformers==4.37.0 # install transformers which supports Qwen2
+```
+
+#### 1.2 Installation on Windows
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.9 libuv
+conda activate llm
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+pip install transformers==4.37.2 # install transformers which supports Qwen2
+```
+
+### 2. Configures OneAPI environment variables
+#### 2.1 Configurations for Linux
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+#### 2.2 Configurations for Windows
+```cmd
+call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
+```
+> Note: Please make sure you are using **CMD** (**Anaconda Prompt** if using conda) to run the command as PowerShell is not supported.
+### 3. Runtime Configurations
+For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
+#### 3.1 Configurations for Linux
+<details>
+
+<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Data Center GPU Max Series</summary>
+
+```bash
+export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export ENABLE_SDP_FUSION=1
+```
+> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
+</details>
+
+#### 3.2 Configurations for Windows
+<details>
+
+<summary>For Intel iGPU</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+set BIGDL_LLM_XMX_DISABLED=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Arc™ A300-Series or Pro A60</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+```
+
+</details>
+
+<details>
+
+<summary>For other Intel dGPU Series</summary>
+
+There is no need to set further environment variables.
+
+</details>
+
+> Note: For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
+### 4. Running examples
+
+```
+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 Qwen1.5 model (e.g. `Qwen/Qwen1.5-7B-Chat`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'Qwen/Qwen1.5-7B-Chat'`.
+- `--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`.
+
+#### Sample Output
+#### [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat)
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+AI是什么？<|im_end|>
+<|im_start|>assistant
+-------------------- Output --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+AI是什么？<|im_end|>
+<|im_start|>assistant
+人工智能（AI）是指通过计算机模拟、延伸和扩展人类智能的学科，其目标是使机器具有学习、推理、感知、理解、交流
+```
+
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+What is AI?<|im_end|>
+<|im_start|>assistant
+-------------------- Output --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+What is AI?<|im_end|>
+<|im_start|>assistant
+AI, or artificial intelligence, refers to the simulation of human intelligence in machines that are programmed to perform tasks that typically require human cognition, such as learning, reasoning
+```
\ No newline at end of file
diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen1.5/generate.py b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen1.5/generate.py
new file mode 100644
index 00000000..423f82aa
--- /dev/null
+++ b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen1.5/generate.py
@@ -0,0 +1,90 @@
+#
+# 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
+
+from transformers import AutoTokenizer
+from bigdl.llm import optimize_model
+import intel_extension_for_pytorch as ipex
+import numpy as np
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Qwen1.5-7B-Chat')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen1.5-7B-Chat",
+                        help='The huggingface repo id for the Qwen1.5 model to be downloaded'
+                             ', or the path to the huggingface 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')
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    
+    from bigdl.llm.transformers import AutoModelForCausalLM
+    # Load model in 4 bit,
+    # which convert the relevant layers in the model into INT4 format
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 load_in_4bit=True,
+                                                 trust_remote_code=True)
+    model = model.to("xpu")
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                              trust_remote_code=True)
+    
+    prompt = args.prompt
+
+    # Generate predicted tokens
+    with torch.inference_mode():
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+            ]
+        text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+            )
+        model_inputs = tokenizer([text], return_tensors="pt").to("xpu")
+        # warmup
+        generated_ids = model.generate(
+            model_inputs.input_ids,
+            max_new_tokens=args.n_predict
+            )
+        
+        st = time.time()
+        generated_ids = model.generate(
+            model_inputs.input_ids,
+            max_new_tokens=args.n_predict
+            )
+        torch.xpu.synchronize()
+        end = time.time()
+        generated_ids = generated_ids.cpu()
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+            ]
+
+        response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Prompt', '-'*20)
+        print(prompt)
+        print('-'*20, 'Output', '-'*20)
+        print(response)
diff --git a/python/llm/example/GPU/PyTorch-Models/Model/qwen1.5/README.md b/python/llm/example/GPU/PyTorch-Models/Model/qwen1.5/README.md
new file mode 100644
index 00000000..d5f3bc93
--- /dev/null
+++ b/python/llm/example/GPU/PyTorch-Models/Model/qwen1.5/README.md
@@ -0,0 +1,143 @@
+# Qwen1.5
+In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate Qwen1.5 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) as a reference InternLM model.
+
+## 0. Requirements
+To run these examples with BigDL-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
+
+## Example: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a Qwen1.5 model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations on Intel GPUs.
+### 1. Install
+#### 1.1 Installation on Linux
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.9
+conda activate llm
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+pip install transformers==4.37.0 # install transformers which supports Qwen2
+```
+
+#### 1.2 Installation on Windows
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.9 libuv
+conda activate llm
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+pip install transformers==4.37.2 # install transformers which supports Qwen2
+```
+
+### 2. Configures OneAPI environment variables
+#### 2.1 Configurations for Linux
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+#### 2.2 Configurations for Windows
+```cmd
+call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
+```
+> Note: Please make sure you are using **CMD** (**Anaconda Prompt** if using conda) to run the command as PowerShell is not supported.
+### 3. Runtime Configurations
+For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
+#### 3.1 Configurations for Linux
+<details>
+
+<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Data Center GPU Max Series</summary>
+
+```bash
+export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export ENABLE_SDP_FUSION=1
+```
+> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
+</details>
+
+#### 3.2 Configurations for Windows
+<details>
+
+<summary>For Intel iGPU</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+set BIGDL_LLM_XMX_DISABLED=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Arc™ A300-Series or Pro A60</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+```
+
+</details>
+
+<details>
+
+<summary>For other Intel dGPU Series</summary>
+
+There is no need to set further environment variables.
+
+</details>
+
+> Note: For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
+### 4. Running examples
+
+```
+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 Qwen1.5 model (e.g. `Qwen/Qwen1.5-7B-Chat`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'Qwen/Qwen1.5-7B-Chat'`.
+- `--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`.
+
+#### Sample Output
+#### [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat)
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+AI是什么？<|im_end|>
+<|im_start|>assistant
+-------------------- Output --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+AI是什么？<|im_end|>
+<|im_start|>assistant
+AI（Artificial Intelligence）是指计算机科学的一个分支，其目标是创建能够理解、学习、推理和自我修正的智能机器。AI系统可以通过
+```
+
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+What is AI?<|im_end|>
+<|im_start|>assistant
+-------------------- Output --------------------
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+What is AI?<|im_end|>
+<|im_start|>assistant
+AI stands for Artificial Intelligence, which is a field of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence, such as learning
+```
\ No newline at end of file
diff --git a/python/llm/example/GPU/PyTorch-Models/Model/qwen1.5/generate.py b/python/llm/example/GPU/PyTorch-Models/Model/qwen1.5/generate.py
new file mode 100644
index 00000000..04b4779d
--- /dev/null
+++ b/python/llm/example/GPU/PyTorch-Models/Model/qwen1.5/generate.py
@@ -0,0 +1,90 @@
+#
+# 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
+
+from transformers import AutoTokenizer
+from bigdl.llm import optimize_model
+import intel_extension_for_pytorch as ipex
+import numpy as np
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Qwen1.5-7B-Chat')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen1.5-7B-Chat",
+                        help='The huggingface repo id for the Qwen1.5 model to be downloaded'
+                             ', or the path to the huggingface 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')
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    
+    from transformers import AutoModelForCausalLM
+    from bigdl.llm import optimize_model
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 trust_remote_code=True,
+                                                 torch_dtype = torch.float16,
+                                                 use_cache=True)
+    model = optimize_model(model)
+    model = model.to("xpu")
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                              trust_remote_code=True)
+    
+    prompt = args.prompt
+    # Generate predicted tokens
+    with torch.inference_mode():
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": prompt}
+            ]
+        text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+            )
+        model_inputs = tokenizer([text], return_tensors="pt").to("xpu")
+        # warmup
+        generated_ids = model.generate(
+            model_inputs.input_ids,
+            max_new_tokens=args.n_predict
+            )
+        
+        st = time.time()
+        generated_ids = model.generate(
+            model_inputs.input_ids,
+            max_new_tokens=args.n_predict
+            )
+        torch.xpu.synchronize()
+        end = time.time()
+        generated_ids = generated_ids.cpu()
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+            ]
+
+        response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Prompt', '-'*20)
+        print(prompt)
+        print('-'*20, 'Output', '-'*20)
+        print(response)