diff --git a/docs/tutorials/releases.md b/docs/tutorials/releases.md
index 1eb6fe712..4da6d5018 100644
--- a/docs/tutorials/releases.md
+++ b/docs/tutorials/releases.md
@@ -42,46 +42,6 @@ We are excited to announce the release of Intel® Extension for PyTorch\* 2.2.0+
 
 **Full Changelog**: https://github.com/intel/intel-extension-for-pytorch/compare/v2.1.100+cpu...v2.2.0+cpu
 
-
-## 2.2.0
-
-### Highlights
-
-- Large Language Model (LLM) optimization:
-Intel® Extension for PyTorch\* provides a new dedicated module, ipex.llm, to host for Large Language Models (LLMs) specific APIs. With ipex.llm, Intel® Extension for PyTorch\* provides comprehensive LLM optimization cross various popular datatypes including FP32/BF16/INT8/INT4. Specifically for low precision, both SmoothQuant and Weight-Only quantization are supported for various scenarios. And user can also run Intel® Extension for PyTorch\* with Tensor Parallel to fit in the multiple ranks or multiple nodes scenarios to get even better performance.
-
-  A typical API under this new module is ipex.llm.optimize, which is designed to optimize transformer-based models within frontend Python modules, with a particular focus on Large Language Models (LLMs). It provides optimizations for both model-wise and content-generation-wise. ipex.llm.optimize is an upgrade API to replace previous ipex.optimize_transformers, which will bring you more consistent LLM experience and performance. Below shows a simple example of ipex.llm.optimize for fp32 or bf16 inference:
-
-  ```python
-  import torch
-  import intel_extension_for_pytorch as ipex
-  import transformers
-
-  model= transformers.AutoModelForCausalLM(model_name_or_path).eval()
-
-  dtype = torch.float # or torch.bfloat16
-  model = ipex.llm.optimize(model, dtype=dtype)
-
-  model.generate(YOUR_GENERATION_PARAMS)
-  ```
-
-  More examples of this API can be found at LLM optimization API.
-
-  Besides the new optimization API for LLM inference, Intel® Extension for PyTorch\* also provides new capability for users to auto-tune a good quantization recipe for running SmoothQuant INT8 with good accuracy. SmoothQuant is a popular method to improve the accuracy of int8 quantization. The new auto-tune API allows automatic global alpha tuning, and automatic layer-by-layer alpha tuning provided by Intel® Neural Compressor for the best INT8 accuracy. More details can be found at SmoothQuant Recipe Tuning API introduction.
-
-  Intel® Extension for PyTorch\* newly optimized many more LLM models including more llama2 variance like llama2-13b/llama2-70b, encoder-decoder model like T5, code generation models like starcoder/codegen, and more like Baichuan, Baichuan2, ChatGLM2, ChatGLM3, mistral, mpt, dolly, etc.. A full list of optimized models can be found at LLM optimization.
-
-- Bug fixing and other optimization
-
-    - Further optimized the performance of LLMs [#2349](https://github.com/intel/intel-extension-for-pytorch/commit/d6d591938aefb9020a8a542a160abe4aeb6b238c) [#2412](https://github.com/intel/intel-extension-for-pytorch/commit/e0399108856c826ad609e5f421021945de30a4bf#diff-11f6a633ad677c6a8b6e8e4462afbe836a853a284e362ba794a8fcbceebc9dc5), [#2469](https://github.com/intel/intel-extension-for-pytorch/commit/aeaeba47bc722d9b18f13f8a78e02092c0a6bb5b), [#2476](https://github.com/intel/intel-extension-for-pytorch/commit/c95eb77398fa131e4ef60be65841ca09a284115d)
-    - Optimized the Flash Attention Operator [#2317](https://github.com/intel/intel-extension-for-pytorch/commit/8d0426c1aebc85620fd417fa7fd4e0f1b357fa3d) [#2334](https://github.com/intel/intel-extension-for-pytorch/commit/efab335b427daf76e01836d520b1d7981de59595) [#2392](https://github.com/intel/intel-extension-for-pytorch/commit/5ed3a2413db5f0a5e53bcca0b3e84a814d87bb50) [#2480](https://github.com/intel/intel-extension-for-pytorch/commit/df2387e976461f6c42e0b90b3544ea76d3132694)
-    - Fixed the static quantization of the ELSER model [#2491](https://github.com/intel/intel-extension-for-pytorch/commit/ac613a73fb395836b210710a6fefdf6d32df3386)
-    - Switched deepspeed to the public release version on PyPI [#2473](https://github.com/intel/intel-extension-for-pytorch/commit/dba7b8c5fc9bfd8e7aa9431efe63499014acd722) [#2511](https://github.com/intel/intel-extension-for-pytorch/commit/94c31ecb3b6f6e77f595ce94dd6d6cbae1db1210)
-    - Upgrade oneDNN to v3.3.4 [#2433](https://github.com/intel/intel-extension-for-pytorch/commit/af9b096070e81b46250172174bb9d12e3e1c6acf)
-
-**Full Changelog**: https://github.com/intel/intel-extension-for-pytorch/compare/v2.1.100+cpu...v2.2.0+cpu
-
-
 ## 2.1.100
 
 ### Highlights