diff --git a/python/llm/example/CPU/PyTorch-Models/More-Data-Types/.keep b/python/llm/example/CPU/PyTorch-Models/More-Data-Types/.keep
deleted file mode 100644
index e69de29b..00000000
diff --git a/python/llm/example/CPU/PyTorch-Models/More-Data-Types/README.md b/python/llm/example/CPU/PyTorch-Models/More-Data-Types/README.md
new file mode 100644
index 00000000..f09a07c2
--- /dev/null
+++ b/python/llm/example/CPU/PyTorch-Models/More-Data-Types/README.md
@@ -0,0 +1,54 @@
+# BigDL-LLM Low Bit Optimization for Large Language Model
+
+In this example, we show how to apply BigDL-LLM low-bit optimizations (including INT8/INT5/INT4) to Llama2 model, and then run inference on the optimized low-bit model.
+
+## 0. Requirements
+To run this example with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../../README.md#system-support) for more information.
+
+## Example: Load Model in Low-Bit Optimization
+In the example [generate.py](./generate.py), we show a basic use case of low-bit optimizations (including INT8/INT5/INT4) on a Llama2 model to predict the next N tokens using `generate()` API. By specifying `--low-bit` argument, you could apply other low-bit optimization (e.g. INT8/INT5) on model.
+### 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
+```
+
+### 2. Run
+Following command will load model in symmetric int 8 optimization:
+```
+python ./generate.py --low-bit sym_int8
+```
+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 Llama2 model (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'`.
+- `--low-bit`: argument defining the low-bit optimization data type, options are sym_int4, asym_int4, sym_int5, asym_int5 or sym_int8. (sym_int4 means symmetric int 4, asym_int4 means asymmetric int 4, etc.). Relevant low bit optimizations will be applied to the model. It is default to be `sym_int8`.
+- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
+- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
+
+### 3 Sample Output
+#### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
+```log
+Inference time: xxxx s
+-------------------- Output --------------------
+### HUMAN:
+What is AI?
+
+### RESPONSE:
+
+AI is a term used to describe the development of computer systems that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing images
+```
+
+#### [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)
+```log
+Inference time: xxxx s
+-------------------- Output --------------------
+### HUMAN:
+What is AI?
+
+### RESPONSE:
+
+AI, or artificial intelligence, refers to the ability of machines to perform tasks that would normally require human intelligence, such as learning, problem-solving,
+```
\ No newline at end of file
diff --git a/python/llm/example/CPU/PyTorch-Models/More-Data-Types/generate.py b/python/llm/example/CPU/PyTorch-Models/More-Data-Types/generate.py
new file mode 100644
index 00000000..5e1b67cc
--- /dev/null
+++ b/python/llm/example/CPU/PyTorch-Models/More-Data-Types/generate.py
@@ -0,0 +1,71 @@
+#
+# 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 AutoModelForCausalLM, LlamaTokenizer
+from bigdl.llm import optimize_model
+
+# you could tune the prompt based on your own model,
+# here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style
+LLAMA2_PROMPT_FORMAT = """### HUMAN:
+{prompt}
+
+### RESPONSE:
+"""
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Example of applying low-bit optimizations on model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-2-7b-chat-hf",
+                        help='The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded'
+                             ', or the path to the huggingface checkpoint folder')
+    parser.add_argument('--low-bit', type=str, default="sym_int8",
+                        choices=['sym_int4', 'asym_int4', 'sym_int5', 'asym_int5', 'sym_int8'],
+                        help='The quantization type the model will convert to.')
+    parser.add_argument('--prompt', type=str, default="What is AI?",
+                        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
+    low_bit = args.low_bit
+
+    # Load model
+    model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
+    
+    # With only one line to enable BigDL-LLM optimization on model
+    # `low_bit` param support `sym_int4`, `asym_int4`, `sym_int5`, `asym_int5` and `sym_int8`
+    # By specifying `low_bit` param, relevant low bit optimizations will be applied to the model
+    model = optimize_model(model, low_bit=low_bit)
+
+    # Load tokenizer
+    tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    
+    # Generate predicted tokens
+    with torch.inference_mode():
+        prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt")
+        st = time.time()
+        output = model.generate(input_ids,
+                                max_new_tokens=args.n_predict)
+        end = time.time()
+        output_str = tokenizer.decode(output[0], skip_special_tokens=True)
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Output', '-'*20)
+        print(output_str)
diff --git a/python/llm/example/CPU/PyTorch-Models/Save-Load/.keep b/python/llm/example/CPU/PyTorch-Models/Save-Load/.keep
deleted file mode 100644
index e69de29b..00000000
diff --git a/python/llm/example/CPU/PyTorch-Models/Save-Load/README.md b/python/llm/example/CPU/PyTorch-Models/Save-Load/README.md
new file mode 100644
index 00000000..16975c92
--- /dev/null
+++ b/python/llm/example/CPU/PyTorch-Models/Save-Load/README.md
@@ -0,0 +1,62 @@
+# Save/Load Low-Bit Models with BigDL-LLM Optimizations
+
+In this example, we show how to save/load model with BigDL-LLM low-bit optimizations (including INT8/INT5/INT4), and then run inference on the optimized low-bit model.
+
+## 0. Requirements
+To run this example with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../../README.md#system-support) for more information.
+
+## Example: Save/Load Model in Low-Bit Optimization
+In the example [generate.py](./generate.py), we show a basic use case of saving/loading model in low-bit optimizations to predict the next N tokens using `generate()` API. Also, saving and loading operations are platform-independent, so you could run it on different platforms.
+### 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
+```
+
+### 2. Run
+If you want to save the optimized low-bit model, run:
+```
+python ./generate.py --save-path path/to/save/model
+```
+
+If you want to load the optimized low-bit model, run:
+```
+python ./generate.py --load-path path/to/load/model
+```
+
+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 Llama2 model (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'`.
+- `--low-bit`: argument defining the low-bit optimization data type, options are sym_int4, asym_int4, sym_int5, asym_int5 or sym_int8. (sym_int4 means symmetric int 4, asym_int4 means asymmetric int 4, etc.). Relevant low bit optimizations will be applied to the model.
+- `--save-path`: argument defining the path to save the low-bit model. Then you can load the low-bit directly.
+- `--load-path`: argument defining the path to load low-bit model.
+- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
+- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
+
+### 3 Sample Output
+#### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
+```log
+Inference time: xxxx s
+-------------------- Output --------------------
+### HUMAN:
+What is AI?
+
+### RESPONSE:
+
+AI is a term used to describe the development of computer systems that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing images
+```
+
+#### [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)
+```log
+Inference time: xxxx s
+-------------------- Output --------------------
+### HUMAN:
+What is AI?
+
+### RESPONSE:
+
+AI, or artificial intelligence, refers to the ability of machines to perform tasks that would typically require human intelligence, such as learning, problem-solving,
+```
\ No newline at end of file
diff --git a/python/llm/example/CPU/PyTorch-Models/Save-Load/generate.py b/python/llm/example/CPU/PyTorch-Models/Save-Load/generate.py
new file mode 100644
index 00000000..ccbee8aa
--- /dev/null
+++ b/python/llm/example/CPU/PyTorch-Models/Save-Load/generate.py
@@ -0,0 +1,81 @@
+#
+# 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 bigdl.llm import optimize_model
+from bigdl.llm.optimize import low_memory_init, load_low_bit
+from transformers import AutoModelForCausalLM, LlamaTokenizer
+
+# you could tune the prompt based on your own model,
+# here the prompt tuning refers to https://huggingface.co/georgesung/llama2_7b_chat_uncensored#prompt-style
+LLAMA2_PROMPT_FORMAT = """### HUMAN:
+{prompt}
+
+### RESPONSE:
+"""
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Example of saving and loading the optimized model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="meta-llama/Llama-2-7b-chat-hf",
+                        help='The huggingface repo id for the Llama2 (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded'
+                             ', or the path to the huggingface checkpoint folder')
+    parser.add_argument('--low-bit', type=str, default="sym_int4",
+                        choices=['sym_int4', 'asym_int4', 'sym_int5', 'asym_int5', 'sym_int8'],
+                        help='The quantization type the model will convert to.')
+    parser.add_argument('--save-path', type=str, default=None,
+                        help='The path to save the low-bit model.')
+    parser.add_argument('--load-path', type=str, default=None,
+                        help='The path to load the low-bit model.')
+    parser.add_argument('--prompt', type=str, default="What is AI?",
+                        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
+    low_bit = args.low_bit
+    load_path = args.load_path
+    if load_path:
+        # Fast and low cost by loading model on meta device
+        with low_memory_init():
+            model = AutoModelForCausalLM.from_pretrained(load_path, torch_dtype="auto", trust_remote_code=True)
+        model = load_low_bit(model, load_path)
+        tokenizer = LlamaTokenizer.from_pretrained(load_path)
+    else:
+        model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
+        model = optimize_model(model, low_bit=low_bit)
+        tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+    # Generate predicted tokens
+    with torch.inference_mode():
+        prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt")
+        st = time.time()
+        output = model.generate(input_ids,
+                                max_new_tokens=args.n_predict)
+        end = time.time()
+        output_str = tokenizer.decode(output[0], skip_special_tokens=True)
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Output', '-'*20)
+        print(output_str)
+
+
+    save_path = args.save_path
+    if save_path:
+        model.save_low_bit(save_path)
+        tokenizer.save_pretrained(save_path)
+        print(f"Model and tokenizer are saved to {save_path}")