diff --git a/README.md b/README.md
index 3d059d6b..b38c9f4e 100644
--- a/README.md
+++ b/README.md
@@ -275,6 +275,7 @@ Over 50 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaM
| Qwen | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen) | [link](python/llm/example/GPU/HuggingFace/LLM/qwen) |
| Qwen1.5 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5) | [link](python/llm/example/GPU/HuggingFace/LLM/qwen1.5) |
| Qwen2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen2) | [link](python/llm/example/GPU/HuggingFace/LLM/qwen2) |
+| Qwen2.5 | | [link](python/llm/example/GPU/HuggingFace/LLM/qwen2.5) |
| Qwen-VL | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen-vl) | [link](python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl) |
| Qwen2-Audio | | [link](python/llm/example/GPU/HuggingFace/Multimodal/qwen2-audio) |
| Aquila | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila) | [link](python/llm/example/GPU/HuggingFace/LLM/aquila) |
diff --git a/python/llm/example/GPU/HuggingFace/LLM/qwen2.5/README.md b/python/llm/example/GPU/HuggingFace/LLM/qwen2.5/README.md
new file mode 100644
index 00000000..12052b33
--- /dev/null
+++ b/python/llm/example/GPU/HuggingFace/LLM/qwen2.5/README.md
@@ -0,0 +1,164 @@
+# Qwen2.5
+In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on Qwen2.5 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct), [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) and [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) as reference Qwen2.5 models.
+
+## 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.
+
+## Example: Predict Tokens using `generate()` API
+In the example [generate.py](./generate.py), we show a basic use case for a Qwen2.5 model to predict the next N tokens using `generate()` API, with IPEX-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=3.11
+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/
+```
+
+#### 1.2 Installation on Windows
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.11 libuv
+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/
+```
+
+### 2. Configures OneAPI environment variables for Linux
+
+> [!NOTE]
+> Skip this step if you are running on Windows.
+
+This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
+
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+### 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
+
+
+For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export SYCL_CACHE_PERSISTENT=1
+```
+
+
+
+
+
+For Intel Data Center GPU Max Series
+
+```bash
+export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export SYCL_CACHE_PERSISTENT=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`.
+
+
+
+
+For Intel iGPU
+
+```bash
+export SYCL_CACHE_PERSISTENT=1
+export BIGDL_LLM_XMX_DISABLED=1
+```
+
+
+
+#### 3.2 Configurations for Windows
+
+
+For Intel iGPU
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+set BIGDL_LLM_XMX_DISABLED=1
+```
+
+
+
+
+
+For Intel Arc™ A-Series Graphics
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+```
+
+
+
+> [!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 Qwen2.5 model (e.g. `Qwen/Qwen2.5-7B-Instruct`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'Qwen/Qwen2.5-7B-Instruct'`.
+- `--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/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+AI是什么？
+-------------------- Output --------------------
+AI是Artificial Intelligence的缩写，意为“人工智能”，是指由人制造出来的系统，能够进行类似于人类智慧的行为，如学习、推理
+```
+
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+What is AI?
+-------------------- Output --------------------
+AI, or Artificial Intelligence, refers to the ability exhibited by machines to imitate human behavior and intelligence. This includes learning, problem-solving, perception, understanding language
+```
+
+##### [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+AI是什么？
+-------------------- Output --------------------
+AI是“人工智能”（Artificial Intelligence）的缩写。它是一门研究如何创建智能机器的学科，这些机器能够执行通常需要人类
+```
+
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+What is AI?
+-------------------- Output --------------------
+Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think, learn, and perform tasks that typically require human intelligence.
+```
+
+##### [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+AI是什么？
+-------------------- Output --------------------
+AI是“人工智能”的简称，是指由人结合科学原理设计，并通过工程实践创造的能够完成特定任务的软件或硬件系统。这些系统
+```
+
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+What is AI?
+-------------------- Output --------------------
+Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that would typically require human intelligence. These tasks can include things like visual perception
+```
\ No newline at end of file
diff --git a/python/llm/example/GPU/HuggingFace/LLM/qwen2.5/generate.py b/python/llm/example/GPU/HuggingFace/LLM/qwen2.5/generate.py
new file mode 100644
index 00000000..d1befbcb
--- /dev/null
+++ b/python/llm/example/GPU/HuggingFace/LLM/qwen2.5/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
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser(description='Predict Tokens using generate() API for Qwen2.5 model')
+ parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen2.5-7B-Instruct",
+ help='The huggingface repo id for the Qwen2.5 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 ipex_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,
+ optimize_model=True,
+ trust_remote_code=True,
+ use_cache=True)
+ model = model.half().to("xpu")
+
+ # Load tokenizer
+ tokenizer = AutoTokenizer.from_pretrained(model_path,
+ trust_remote_code=True)
+
+ prompt = args.prompt
+
+ # Generate predicted tokens
+ with torch.inference_mode():
+ # The following code for generation is adapted from https://huggingface.co/Qwen/Qwen2.5-7B-Instruct#quickstart
+ 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/HuggingFace/LLM/qwen2/README.md b/python/llm/example/GPU/HuggingFace/LLM/qwen2/README.md
index f0ae5e3e..8ade27f6 100644
--- a/python/llm/example/GPU/HuggingFace/LLM/qwen2/README.md
+++ b/python/llm/example/GPU/HuggingFace/LLM/qwen2/README.md
@@ -135,7 +135,7 @@ AI, or Artificial Intelligence, refers to the simulation of human intelligence i
##### [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct)
```log
-Inference time: 0.33887791633605957 s
+Inference time: xxxx s
-------------------- Prompt --------------------
AI是什么？
-------------------- Output --------------------
@@ -143,7 +143,7 @@ AI是人工智能的简称，是一种计算机科学和技术领域，旨在使
```
```log
-Inference time: 0.340407133102417 s
+Inference time: xxxx s
-------------------- Prompt --------------------
What is AI?
-------------------- Output --------------------