diff --git a/docs/readthedocs/source/doc/LLM/Overview/KeyFeatures/optimize_model.md b/docs/readthedocs/source/doc/LLM/Overview/KeyFeatures/optimize_model.md
index ac510688..e997d32c 100644
--- a/docs/readthedocs/source/doc/LLM/Overview/KeyFeatures/optimize_model.md
+++ b/docs/readthedocs/source/doc/LLM/Overview/KeyFeatures/optimize_model.md
@@ -2,6 +2,8 @@
 
 In general, you just need one-line `optimize_model` to easily optimize any loaded PyTorch model, regardless of the library or API you are using. With BigDL-LLM, PyTorch models (in FP16/BF16/FP32) can be optimized with low-bit quantizations (supported precisions include INT4, INT5, INT8, etc).
 
+### Optimize model
+
 First, use any PyTorch APIs you like to load your model. To help you better understand the process, here we use [Hugging Face Transformers](https://huggingface.co/docs/transformers/index) library `LlamaForCausalLM` to load a popular model [Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) as an example:
 
 ```python
@@ -20,8 +22,48 @@ model = optimize_model(model)
 
 After optimizing the model, BigDL-LLM does not require any change in the inference code. You can use any libraries to run the optimized model with very low latency.
 
+### More Precisions
+
+In the [Optimize Model](#optimize-model), symmetric INT4 optimization is applied by default. You may apply other low bit optimizations (INT5, INT8, etc) by specifying the ``low_bit`` parameter.
+
+Currently, ``low_bit`` supports options 'sym_int4', 'asym_int4', 'sym_int5', 'asym_int5' or 'sym_int8', in which 'sym' and 'asym' differentiate between symmetric and asymmetric quantization. Symmetric quantization allocates bits for positive and negative values equally, whereas asymmetric quantization allows different bit allocations for positive and negative values.
+
+You may apply symmetric INT8 optimization as follows:
+
+```python
+from bigdl.llm import optimize_model
+
+# Apply symmetric INT8 optimization
+model = optimize_model(model, low_bit="sym_int8")
+```
+
+### Save & Load Optimized Model
+
+The loading process of the original model may be time-consuming and memory-intensive. For example, the [Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) model is stored with float16 precision, resulting in large memory usage when loaded using `LlamaForCausalLM`. To avoid high resource consumption and expedite loading process, you can use `save_low_bit` to store the model after low-bit optimization. Then, in subsequent uses, you can opt to use the `load_low_bit` API to directly load the optimized model. Besides, saving and loading operations are platform-independent, regardless of their operating systems.
+#### Save
+
+Continuing with the [example of Llama-2-7b-chat-hf](#optimize-model), we can save the previously optimized model as follows:
+```python
+saved_dir='./llama-2-bigdl-llm-4-bit'
+model.save_low_bit(saved_dir)
+```
+#### Load
+
+We recommend to use the context manager `low_memory_init` to quickly initiate a model instance with low cost, and then use `load_low_bit` to load the optimized low-bit model as follows:
+```python
+from bigdl.llm.optimize import low_memory_init, load_low_bit
+with low_memory_init(): # Fast and low cost by loading model on meta device
+   model = LlamaForCausalLM.from_pretrained(saved_dir,
+                                            torch_dtype="auto",
+                                            trust_remote_code=True)
+model = load_low_bit(model, saved_dir) # Load the optimized model
+```
+
+
 ```eval_rst
 .. seealso::
 
-   * For more detailed usage of ``optimize_model``, please refer to the `API documentation <https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/LLM/optimize.html>`_.
+   * Please refer to the `API documentation <https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/LLM/optimize.html>`_ for more details.
+
+   * We also provide detailed examples on how to run PyTorch models (e.g., Openai Whisper, LLaMA2, ChatGLM2, Falcon, MPT, Baichuan2, etc.) using BigDL-LLM. See the complete CPU examples `here <https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/PyTorch-Models>`_ and GPU examples `here <https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/GPU/PyTorch-Models>`_.
 ```