diff --git a/README.md b/README.md
index ab006507..e33c8c15 100644
--- a/README.md
+++ b/README.md
@@ -1,31 +1,42 @@
-## IPEX-LLM
+> [!IMPORTANT]
+> ***`bigdl-llm` has now become `ipex-llm` (see the migration guide [here](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/bigdl_llm_migration.html)); you may find the original `BigDL` project [here](https://github.com/intel-analytics/BigDL-2.x).***
+ 
+---
 
-**`ipex-llm`** is a library for running **LLM** (large language model) on Intel **XPU** (from *Laptop* to *GPU* to *Cloud*) using **INT4/FP4/INT8/FP8** with very low latency[^1] (for any **PyTorch** model).
+# 💫 IPEX-LLM
+**`IPEX-LLM`** is a PyTorch library for running **LLM** on Intel CPU and GPU *(e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max)* with very low latency[^1]. 
+> [!NOTE]
+> - *It is built on top of **Intel Extension for PyTorch** (**`IPEX`**), as well as the excellent work of **`llama.cpp`**, **`bitsandbytes`**, **`vLLM`**, **`qlora`**, **`AutoGPTQ`**, **`AutoAWQ`**, etc.*
+> - *It provides seamless integration with [llama.cpp](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html), [Text-Generation-WebUI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/webui_quickstart.html), [HuggingFace tansformers](python/llm/example/GPU/HF-Transformers-AutoModels), [HuggingFace PEFT](python/llm/example/GPU/LLM-Finetuning), [LangChain](python/llm/example/GPU/LangChain), [LlamaIndex](python/llm/example/GPU/LlamaIndex), [DeepSpeed-AutoTP](python/llm/example/GPU/Deepspeed-AutoTP), [vLLM](python/llm/example/GPU/vLLM-Serving), [FastChat](python/llm/src/ipex_llm/serving/fastchat), [HuggingFace TRL](python/llm/example/GPU/LLM-Finetuning/DPO), [AutoGen](python/llm/example/CPU/Applications/autogen), [ModeScope](python/llm/example/GPU/ModelScope-Models), etc.* 
+> - ***50+ models** have been optimized/verified on `ipex-llm` (including LLaMA2, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM, Baichuan, Qwen, RWKV, and more); see the complete list [here](#verified-models).*
 
-> *It is built on the excellent work of [llama.cpp](https://github.com/ggerganov/llama.cpp), [bitsandbytes](https://github.com/TimDettmers/bitsandbytes), [qlora](https://github.com/artidoro/qlora), [gptq](https://github.com/IST-DASLab/gptq), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [awq](https://github.com/mit-han-lab/llm-awq), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), [vLLM](https://github.com/vllm-project/vllm), [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), [gptq_for_llama](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [chatglm.cpp](https://github.com/li-plus/chatglm.cpp), [redpajama.cpp](https://github.com/togethercomputer/redpajama.cpp), [gptneox.cpp](https://github.com/byroneverson/gptneox.cpp), [bloomz.cpp](https://github.com/NouamaneTazi/bloomz.cpp/), etc.*
-
-### Latest update 🔥 
-- [2024/03] **LangChain** added support for `ipex-llm`; see the details [here](https://python.langchain.com/docs/integrations/llms/bigdl).
+## Latest Update 🔥 
+- [2024/03] `bigdl-llm` has now become `ipex-llm` (see the migration guide [here](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/bigdl_llm_migration.html)); you may find the original `BigDL` project [here](https://github.com/intel-analytics/bigdl-2.x).
 - [2024/02] `ipex-llm` now supports directly loading model from [ModelScope](python/llm/example/GPU/ModelScope-Models) ([魔搭](python/llm/example/CPU/ModelScope-Models)).
 - [2024/02] `ipex-llm` added inital **INT2** support (based on llama.cpp [IQ2](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF-IQ2) mechanism), which makes it possible to run large-size LLM (e.g., Mixtral-8x7B) on Intel GPU with 16GB VRAM.
 - [2024/02] Users can now use `ipex-llm` through [Text-Generation-WebUI](https://github.com/intel-analytics/text-generation-webui) GUI.
 - [2024/02] `ipex-llm` now supports *[Self-Speculative Decoding](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Inference/Self_Speculative_Decoding.html)*, which in practice brings **~30% speedup** for FP16 and BF16 inference latency on Intel [GPU](python/llm/example/GPU/Speculative-Decoding) and [CPU](python/llm/example/CPU/Speculative-Decoding) respectively.
-- [2024/02] `ipex-llm` now supports a comprehensive list of LLM finetuning on Intel GPU (including [LoRA](python/llm/example/GPU/LLM-Finetuning/LoRA), [QLoRA](python/llm/example/GPU/LLM-Finetuning/QLoRA), [DPO](python/llm/example/GPU/LLM-Finetuning/DPO), [QA-LoRA](python/llm/example/GPU/LLM-Finetuning/QA-LoRA) and [ReLoRA](python/llm/example/GPU/LLM-Finetuning/ReLora)).
+- [2024/02] `ipex-llm` now supports a comprehensive list of LLM **finetuning** on Intel GPU (including [LoRA](python/llm/example/GPU/LLM-Finetuning/LoRA), [QLoRA](python/llm/example/GPU/LLM-Finetuning/QLoRA), [DPO](python/llm/example/GPU/LLM-Finetuning/DPO), [QA-LoRA](python/llm/example/GPU/LLM-Finetuning/QA-LoRA) and [ReLoRA](python/llm/example/GPU/LLM-Finetuning/ReLora)).
 - [2024/01] Using `ipex-llm` [QLoRA](python/llm/example/GPU/LLM-Finetuning/QLoRA), we managed to finetune LLaMA2-7B in **21 minutes** and LLaMA2-70B in **3.14 hours** on 8 Intel Max 1550 GPU for [Standford-Alpaca](python/llm/example/GPU/LLM-Finetuning/QLoRA/alpaca-qlora) (see the blog [here](https://www.intel.com/content/www/us/en/developer/articles/technical/finetuning-llms-on-intel-gpus-using-bigdl-llm.html)).
-- [2024/01] 🔔🔔🔔 ***The default `ipex-llm` GPU Linux installation has switched from PyTorch 2.0 to PyTorch 2.1, which requires new oneAPI and GPU driver versions. (See the [GPU installation guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html) for more details.)***
+<details><summary>More updates</summary>
+<br/>
+ 
 - [2023/12] `ipex-llm` now supports [ReLoRA](python/llm/example/GPU/LLM-Finetuning/ReLora) (see *["ReLoRA: High-Rank Training Through Low-Rank Updates"](https://arxiv.org/abs/2307.05695)*).
-- [2023/12] `ipex-llm` now supports [Mixtral-8x7B](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mixtral) on both Intel [GPU](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mixtral) and [CPU](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral).
-- [2023/12] `ipex-llm` now supports [QA-LoRA](python/llm/example/GPU/LLM-Finetuning/QA-LoRA) (see *["QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models"](https://arxiv.org/abs/2309.14717)*).
+- [2023/12] `ipex-llm` now supports [Mixtral-8x7B](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mixtral) on both Intel [GPU](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mixtral) and [CPU](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral). 
+- [2023/12] `ipex-llm` now supports [QA-LoRA](python/llm/example/GPU/LLM-Finetuning/QA-LoRA) (see *["QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models"](https://arxiv.org/abs/2309.14717)*). 
 - [2023/12] `ipex-llm` now supports [FP8 and FP4 inference](python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types) on Intel ***GPU***.
 - [2023/11] Initial support for directly loading [GGUF](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF), [AWQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ) and [GPTQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GPTQ) models into `ipex-llm` is available.
 - [2023/11] `ipex-llm` now supports [vLLM continuous batching](python/llm/example/GPU/vLLM-Serving) on both Intel [GPU](python/llm/example/GPU/vLLM-Serving) and [CPU](python/llm/example/CPU/vLLM-Serving).
 - [2023/10] `ipex-llm` now supports [QLoRA finetuning](python/llm/example/GPU/LLM-Finetuning/QLoRA) on both Intel [GPU](python/llm/example/GPU/LLM-Finetuning/QLoRA) and [CPU](python/llm/example/CPU/QLoRA-FineTuning).
 - [2023/10] `ipex-llm` now supports [FastChat serving](python/llm/src/ipex_llm/llm/serving) on on both Intel CPU and GPU.
 - [2023/09] `ipex-llm` now supports [Intel GPU](python/llm/example/GPU) (including iGPU, Arc, Flex and MAX).
-- [2023/09] `ipex-llm` [tutorial](https://github.com/intel-analytics/ipex-llm-tutorial) is released.
-- [2023/09] Over 40 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaMA2, ChatGLM2/ChatGLM3, Mistral, Falcon, MPT, LLaVA, WizardCoder, Dolly, Whisper, Baichuan/Baichuan2, InternLM, Skywork, QWen/Qwen-VL, Aquila, MOSS,* and more; see the complete list [here](#verified-models).
+- [2023/09] `ipex-llm` [tutorial](https://github.com/intel-analytics/bigdl-llm-tutorial) is released.
+ 
+</details> 
 
-### `ipex-llm` Demos
+[^1]: Performance varies by use, configuration and other factors. `ipex-llm` may not optimize to the same degree for non-Intel products. Learn more at www.Intel.com/PerformanceIndex.
+
+## `ipex-llm` Demos
 See the ***optimized performance*** of `chatglm2-6b` and `llama-2-13b-chat` models on 12th Gen Intel Core CPU and Intel Arc GPU below.
 
 <table width="100%">
@@ -55,146 +66,106 @@ See the ***optimized performance*** of `chatglm2-6b` and `llama-2-13b-chat` mode
   </tr>
 </table>
 
-### `ipex-llm` quickstart
+## `ipex-llm` Quickstart
+### Install `ipex-llm`
+- [Windows GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_windows_gpu.html): installing `ipex-llm` on Windows with Intel GPU
+- [Linux GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_linux_gpu.html): installing `ipex-llm` on Linux with Intel GPU
+- [Docker](docker/llm): using `ipex-llm` dockers on Intel CPU and GPU
+- *For more details, please refer to the [installation guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install.html)*
 
-- [Windows GPU installation](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_windows_gpu.html)
-- [Run IPEX-LLM in Text-Generation-WebUI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/webui_quickstart.html)
-- [Run IPEX-LLM using Docker](docker/llm)
-- [CPU INT4](#cpu-int4)
-- [GPU INT4](#gpu-int4)
-- [More Low-Bit support](#more-low-bit-support)
-- [Verified models](#verified-models)
+### Run `ipex-llm`
+- [llama.cpp](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html): running **ipex-llm for llama.cpp** (*using C++ interface of `ipex-llm` as an accelerated backend for `llama.cpp` on Intel GPU*)
+- [vLLM](python/llm/example/GPU/vLLM-Serving): running `ipex-llm` in `vLLM` on both Intel [GPU](python/llm/example/GPU/vLLM-Serving) and [CPU](python/llm/example/CPU/vLLM-Serving)
+- [FastChat](python/llm/src/ipex_llm/serving/fastchat): running `ipex-llm` in `FastChat` serving on on both Intel GPU and CPU
+- [LangChain-Chatchat RAG](https://github.com/intel-analytics/Langchain-Chatchat): running `ipex-llm` in `LangChain-Chatchat` (*Knowledge Base QA using **RAG** pipeline*)
+- [Text-Generation-WebUI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/webui_quickstart.html): running `ipex-llm` in `oobabooga` **WebUI**
+- [Benchmarking](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/benchmark_quickstart.html): running  (latency and throughput) benchmarks for `ipex-llm` on Intel CPU and GPU
 
-#### CPU INT4
-##### Install
-You may install **`ipex-llm`** on Intel CPU as follows:
-> Note: See the [CPU installation guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install_cpu.html) for more details.
-```bash
-pip install --pre --upgrade ipex-llm[all]
-```
-> Note: `ipex-llm` has been tested on Python 3.9, 3.10 and 3.11
+### Code Examples
+- Low bit inference
+  - [INT4 inference](python/llm/example/GPU/HF-Transformers-AutoModels/Model): **INT4** LLM inference on Intel [GPU](python/llm/example/GPU/HF-Transformers-AutoModels/Model) and [CPU](python/llm/example/CPU/HF-Transformers-AutoModels/Model)
+  - [FP8/FP4 inference](python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types): **FP8** and **FP4** LLM inference on Intel [GPU](python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types)
+  - [INT8 inference](python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types): **INT8** LLM inference on Intel [GPU](python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types) and [CPU](python/llm/example/GPU/HF-Transformers-AutoModels/More-Data-Types)
+  - [INT2 inference](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF-IQ2): **INT2** LLM inference (based on llama.cpp IQ2 mechanism) on Intel [GPU](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF-IQ2)
+- FP16/BF16 inference
+  - **FP16** LLM inference on Intel [GPU](python/llm/example/GPU/Speculative-Decoding), with possible [self-speculative decoding](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Inference/Self_Speculative_Decoding.html) optimization
+  - **BF16** LLM inference on Intel [CPU](python/llm/example/CPU/Speculative-Decoding), with possible [self-speculative decoding](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Inference/Self_Speculative_Decoding.html) optimization 
+- Save and load
+  - [Low-bit models](python/llm/example/CPU/HF-Transformers-AutoModels/Save-Load): saving and loading `ipex-llm` low-bit models
+  - [GGUF](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF): directly loading GGUF models into `ipex-llm`
+  - [AWQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ): directly loading AWQ models into `ipex-llm`
+  - [GPTQ](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/QPTQ): directly loading GPTQ models into `ipex-llm`
+- Finetuning
+  - LLM finetuning on Intel [GPU](python/llm/example/GPU/LLM-Finetuning), including [LoRA](python/llm/example/GPU/LLM-Finetuning/LoRA), [QLoRA](python/llm/example/GPU/LLM-Finetuning/QLoRA), [DPO](python/llm/example/GPU/LLM-Finetuning/DPO), [QA-LoRA](python/llm/example/GPU/LLM-Finetuning/QA-LoRA) and [ReLoRA](python/llm/example/GPU/LLM-Finetuning/ReLora)
+  - QLoRA finetuning on Intel [CPU](python/llm/example/CPU/QLoRA-FineTuning)
+- Integration with community libraries
+  - [HuggingFace tansformers](python/llm/example/GPU/HF-Transformers-AutoModels)
+  - [Standard PyTorch model](python/llm/example/GPU/PyTorch-Models)
+  - [DeepSpeed-AutoTP](python/llm/example/GPU/Deepspeed-AutoTP)
+  - [HuggingFace PEFT](python/llm/example/GPU/LLM-Finetuning/HF-PEFT)
+  - [HuggingFace TRL](python/llm/example/GPU/LLM-Finetuning/DPO)
+  - [LangChain](python/llm/example/GPU/LangChain)
+  - [LlamaIndex](python/llm/example/GPU/LlamaIndex)
+  - [AutoGen](python/llm/example/CPU/Applications/autogen)
+  - [ModeScope](python/llm/example/GPU/ModelScope-Models)
+- [Tutorials](https://github.com/intel-analytics/bigdl-llm-tutorial)
 
-##### Run Model
-You may apply INT4 optimizations to any Hugging Face *Transformers* models as follows.
+*For more details, please refer to the `ipex-llm` document [website](https://ipex-llm.readthedocs.io/).*
 
-```python
-#load Hugging Face Transformers model with INT4 optimizations
-from ipex_llm.transformers import AutoModelForCausalLM
-model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
-
-#run the optimized model on CPU
-from transformers import AutoTokenizer
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-input_ids = tokenizer.encode(input_str, ...)
-output_ids = model.generate(input_ids, ...)
-output = tokenizer.batch_decode(output_ids)
-```
-*See the complete examples [here](python/llm/example/CPU/HF-Transformers-AutoModels/Model).*
-
-#### GPU INT4
-##### Install
-You may install **`ipex-llm`** on Intel GPU as follows:
-> Note: See the [GPU installation guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html) for more details.
-```bash
-# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
-pip install --pre --upgrade ipex-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
-```
-> Note: `ipex-llm` has been tested on Python 3.9, 3.10 and 3.11
-
-##### Run Model
-You may apply INT4 optimizations to any Hugging Face *Transformers* models as follows.
-
-```python
-#load Hugging Face Transformers model with INT4 optimizations
-from ipex_llm.transformers import AutoModelForCausalLM
-model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
-
-#run the optimized model on Intel GPU
-model = model.to('xpu')
-
-from transformers import AutoTokenizer
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-input_ids = tokenizer.encode(input_str, ...).to('xpu')
-output_ids = model.generate(input_ids, ...)
-output = tokenizer.batch_decode(output_ids.cpu())
-```
-*See the complete examples [here](python/llm/example/GPU).*
-
-#### More Low-Bit Support
-##### Save and load
-
-After the model is optimized using `ipex-llm`, you may save and load the model as follows:
-```python
-model.save_low_bit(model_path)
-new_model = AutoModelForCausalLM.load_low_bit(model_path)
-```
-*See the complete example [here](python/llm/example/CPU/HF-Transformers-AutoModels/Save-Load).*
-
-##### Additonal data types
-
-In addition to INT4, You may apply other low bit optimizations (such as *INT8*, *INT5*, *NF4*, etc.) as follows: 
-```python
-model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_low_bit="sym_int8")
-```
-*See the complete example [here](python/llm/example/CPU/HF-Transformers-AutoModels/More-Data-Types).*
-
-#### Verified Models
-Over 40 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaMA2, ChatGLM/ChatGLM2, Mistral, Falcon, MPT, Baichuan/Baichuan2, InternLM, QWen* and more; see the example list below.
-
-| Model                                    | CPU Example                              | GPU Example                              |
-| ---------------------------------------- | ---------------------------------------- | ---------------------------------------- |
-| LLaMA *(such as Vicuna, Guanaco, Koala, Baize, WizardLM, etc.)* | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/vicuna) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/vicuna) |
-| LLaMA 2                                  | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama2) | [link1](python/llm/example/GPU/HF-Transformers-AutoModels/Model/llama2), [link2-low GPU memory example](python/llm/example/GPU/PyTorch-Models/Model/llama2#example-2---low-memory-version-predict-tokens-using-generate-api) |
-| ChatGLM                                  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm) |                                          |
-| ChatGLM2                                 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm2) |
-| ChatGLM3                                 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm3) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm3) |
-| Mistral                                  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mistral) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mistral) |
-| Mixtral                                  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mixtral) |
-| Falcon                                   | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/falcon) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/falcon) |
-| MPT                                      | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mpt) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mpt) |
-| Dolly-v1                                 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v1) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/dolly-v1) |
-| Dolly-v2                                 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/dolly-v2) |
-| Replit Code                              | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/replit) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/replit) |
-| RedPajama                                | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/redpajama) |                                          |
-| Phoenix                                  | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phoenix) |                                          |
-| StarCoder                                | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/starcoder) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/starcoder) |
-| Baichuan                                 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/baichuan) |
-| Baichuan2                                | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/baichuan2) |
-| InternLM                                 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/internlm) |
-| Qwen                                     | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen) |
-| Qwen1.5                                  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen1.5) |
-| Qwen-VL                                  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen-vl) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen-vl) |
-| Aquila                                   | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/aquila) |
-| Aquila2                                  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/aquila2) |
-| MOSS                                     | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/moss) |                                          |
-| Whisper                                  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/whisper) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/whisper) |
-| Phi-1_5                                  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-1_5) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-1_5) |
-| Flan-t5                                  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/flan-t5) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/flan-t5) |
-| LLaVA                                    | [link](python/llm/example/CPU/PyTorch-Models/Model/llava) | [link](python/llm/example/GPU/PyTorch-Models/Model/llava) |
-| CodeLlama                                | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/codellama) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/codellama) |
-| Skywork                                  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/skywork) |                                          |
-| InternLM-XComposer                       | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm-xcomposer) |                                          |
-| WizardCoder-Python                       | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/wizardcoder-python) |                                          |
-| CodeShell                                | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/codeshell) |                                          |
-| Fuyu                                     | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/fuyu) |                                          |
-| Distil-Whisper                           | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/distil-whisper) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/distil-whisper) |
-| Yi                                       | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/yi) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/yi) |
-| BlueLM                                   | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/bluelm) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/bluelm) |
-| Mamba                                    | [link](python/llm/example/CPU/PyTorch-Models/Model/mamba) | [link](python/llm/example/GPU/PyTorch-Models/Model/mamba) |
-| SOLAR                                    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/solar) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/solar) |
-| Phixtral                                 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phixtral) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phixtral) |
-| InternLM2                                | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/internlm2) |
-| RWKV4                                    |                                          | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/rwkv4) |
-| RWKV5                                    |                                          | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/rwkv5) |
-| 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) |
-| DeepSeek-MoE                             | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/deepseek-moe) |                                          |
-| Ziya-Coding-34B-v1.0                     | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/ziya) |                                          |
-| Phi-2                                    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-2) |
-| Yuan2                                    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/yuan2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/yuan2) |
-| Gemma                                    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/gemma) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/gemma) |
-| DeciLM-7B                                | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/deciLM-7b) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/deciLM-7b) |
-| Deepseek                                 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/deepseek) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/deepseek) |
-
-
-***For more details, please refer to the `ipex-llm` [Document](https://test-ipex-llm.readthedocs.io/en/main/doc/LLM/index.html), [Readme](python/llm), [Tutorial](https://github.com/intel-analytics/ipex-llm-tutorial) and [API Doc](https://ipex-llm.readthedocs.io/en/latest/doc/PythonAPI/LLM/index.html).***
+## Verified Models
+Over 50 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaMA2, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM2/ChatGLM3, Baichuan/Baichuan2, Qwen/Qwen-1.5, InternLM* and more; see the list below.
+  
+| Model      | CPU Example                                                    | GPU Example                                                     |
+|------------|----------------------------------------------------------------|-----------------------------------------------------------------|
+| LLaMA *(such as Vicuna, Guanaco, Koala, Baize, WizardLM, etc.)* | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/vicuna) |[link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/vicuna)|
+| LLaMA 2    | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/llama2)  |
+| ChatGLM    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm)   |    | 
+| ChatGLM2   | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm2)  | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm2)   |
+| ChatGLM3   | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm3)  | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm3)   |
+| Mistral    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mistral)   | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mistral)    |
+| Mixtral    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral)   | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mixtral)    |
+| Falcon     | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/falcon)    | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/falcon)     |
+| MPT        | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mpt)       | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mpt)        |
+| Dolly-v1   | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v1)  | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/dolly-v1)   | 
+| Dolly-v2   | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v2)  | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/dolly-v2)   | 
+| Replit Code| [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/replit)    | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/replit)     |
+| RedPajama  | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/redpajama) |    | 
+| Phoenix    | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phoenix)   |    | 
+| StarCoder  | [link1](python/llm/example/CPU/Native-Models), [link2](python/llm/example/CPU/HF-Transformers-AutoModels/Model/starcoder) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/starcoder) | 
+| Baichuan   | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan)  | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/baichuan)   |
+| Baichuan2  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/baichuan2)  |
+| InternLM   | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm)  | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/internlm)   |
+| Qwen       | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen)      | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen)       |
+| Qwen1.5 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen1.5) |
+| Qwen-VL    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen-vl)   | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen-vl)    |
+| Aquila     | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila)    | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/aquila)     |
+| Aquila2     | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila2)    | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/aquila2)     |
+| MOSS       | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/moss)      |    | 
+| Whisper    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/whisper)   | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/whisper)    |
+| Phi-1_5    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-1_5)   | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-1_5)    |
+| Flan-t5    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/flan-t5)   | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/flan-t5)    |
+| LLaVA      | [link](python/llm/example/CPU/PyTorch-Models/Model/llava)                 | [link](python/llm/example/GPU/PyTorch-Models/Model/llava)                  |
+| CodeLlama  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/codellama) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/codellama)  |
+| Skywork      | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/skywork)                 |    |
+| InternLM-XComposer  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm-xcomposer)   |    |
+| WizardCoder-Python | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/wizardcoder-python) | |
+| CodeShell | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/codeshell) | |
+| Fuyu      | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/fuyu) | |
+| Distil-Whisper | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/distil-whisper) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/distil-whisper) |
+| Yi | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/yi) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/yi) |
+| BlueLM | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/bluelm) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/bluelm) |
+| Mamba | [link](python/llm/example/CPU/PyTorch-Models/Model/mamba) | [link](python/llm/example/GPU/PyTorch-Models/Model/mamba) |
+| SOLAR | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/solar) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/solar) |
+| Phixtral | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phixtral) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phixtral) |
+| InternLM2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/internlm2) |
+| RWKV4 |  | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/rwkv4) |
+| RWKV5 |  | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/rwkv5) |
+| 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) |
+| DeepSeek-MoE | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/deepseek-moe) |  |
+| Ziya-Coding-34B-v1.0 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/ziya) | |
+| Phi-2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phi-2) |
+| Yuan2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/yuan2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/yuan2) |
+| Gemma | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/gemma) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/gemma) |
+| DeciLM-7B | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/deciLM-7b) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/deciLM-7b) |
+| Deepseek | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/deepseek) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/deepseek) |
diff --git a/docs/readthedocs/source/doc/LLM/Quickstart/bigdl_llm_migration.md b/docs/readthedocs/source/doc/LLM/Quickstart/bigdl_llm_migration.md
index 00db27a5..4f32cc87 100644
--- a/docs/readthedocs/source/doc/LLM/Quickstart/bigdl_llm_migration.md
+++ b/docs/readthedocs/source/doc/LLM/Quickstart/bigdl_llm_migration.md
@@ -1,28 +1,17 @@
-# bigdl-llm upgrade guide
-## Upgrade bigdl-llm to ipex-llm
-Need to uninstall bigdl-llm and install ipex-llm first.
+# `bigdl-llm` Migration Guide
+## Upgrade `bigdl-llm` package to `ipex-llm`
+First uninstall `bigdl-llm` and install `ipex-llm`.
 ```bash
 pip uninstall -y bigdl-llm
 pip install --pre --upgrade ipex-llm[all] # for cpu
 pip install --pre --upgrade ipex-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu # for xpu
 ```
-## Run bigdl-llm example code
-Choose a way to modify the example code to use.
+## Migrate `bigdl-llm` code to `ipex-llm`
+There are two options to migrate `bigdl-llm` code to `ipex-llm`.
 
-### 1.Run bigdl-llm example code with ipex-llm in compatible mode
-Minimal change: Use `BIGDL_COMPATIBLE_MODE` to run, need to add `import ipex_llm` at the 
-beginning of code with bigdl-llm examples.
-```python
-# need to add before import bigdl.llm
-import ipex_llm
-from bigdl.llm.transformers import AutoModelForCausalLM
-model = AutoModelForCausalLM.from_pretrained(model_path,
-                                             load_in_4bit=True,
-                                             trust_remote_code=True)
-```
+### 1. Upgrade `bigdl-llm` code to `ipex-llm`
+To upgrade `bigdl-llm` code to `ipex-llm`, simply replace all `bigdl.llm` with `ipex_llm`:
 
-### 2.Upgrade bigdl-llm example code to ipex-llm
-Need to replace all `bigdl.llm` with `ipex_llm`.
 ```python
 #from bigdl.llm.transformers import AutoModelForCausalLM
 from ipex_llm.transformers import AutoModelForCausalLM
@@ -31,3 +20,15 @@ model = AutoModelForCausalLM.from_pretrained(model_path,
                                              trust_remote_code=True)
 ```
 
+### 2. Run `bigdl-llm` code in compatible mode (experimental)
+To run in the compatible mode, simply add `import ipex_llm` at the beginning of the existing `bigdl-llm` code:
+
+```python
+# need to add the below line before "import bigdl.llm"
+import ipex_llm
+from bigdl.llm.transformers import AutoModelForCausalLM
+model = AutoModelForCausalLM.from_pretrained(model_path,
+                                             load_in_4bit=True,
+                                             trust_remote_code=True)
+```
+