diff --git a/python/llm/example/CPU/Speculative-Decoding/baichuan2/README.md b/python/llm/example/CPU/Speculative-Decoding/baichuan2/README.md
index 2b8dcaea..e5895024 100644
--- a/python/llm/example/CPU/Speculative-Decoding/baichuan2/README.md
+++ b/python/llm/example/CPU/Speculative-Decoding/baichuan2/README.md
@@ -63,33 +63,20 @@ First token latency x.xxxxs
 
 ### 4. Accelerate with BIGDL_OPT_IPEX
 
-To accelerate speculative decoding on CPU, optionally, you can install our validated version of [IPEX 2.3.0+git0c63936](https://github.com/intel/intel-extension-for-pytorch/tree/0c63936d7a6740679987920367ae2e0cdb375b2e) by following steps: (Other versions of IPEX may have some conflicts and can not accelerate speculative decoding correctly.)
+To accelerate speculative decoding on CPU, optionally, you can install our validated version of [IPEX 2.2.0+cpu](https://github.com/intel/intel-extension-for-pytorch/tree/v2.2.0%2Bcpu) refering to [IPEX's installation guide](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=cpu&version=v2.2.0%2Bcpu), or by the following steps: (Other versions of IPEX may have some conflicts and can not accelerate speculative decoding correctly.)
 
-#### 4.1 Download IPEX installation script
+#### 4.1 Install IPEX 2.2.0+cpu
 ```bash
-# Depend on Conda and GCC 12.3
-wget https://raw.githubusercontent.com/intel/intel-extension-for-pytorch/0c63936d7a6740679987920367ae2e0cdb375b2e/scripts/compile_bundle.sh
-```
-
-#### 4.2 Activate your conda environment
-```bash
-conda activate <your_conda_env>
-```
-#### 4.3 Set VER_IPEX in compile_bundle.sh to 0c63936d7a6740679987920367ae2e0cdb375b2e
-```bash
-sed -i 's/VER_IPEX=main/VER_IPEX=0c63936d7a6740679987920367ae2e0cdb375b2e/g' "compile_bundle.sh"
-```
-
-#### 4.4 Install IPEX and other dependencies
-```bash
-# Install IPEX 2.3.0+git0c63936
-bash compile_bundle.sh 
+python -m pip install torch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cpu
+python -m pip install intel-extension-for-pytorch==2.2.0
+python -m pip install oneccl_bind_pt==2.2.0 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/cpu/us/
+# if there is any installation problem for oneccl_binding, you can also find suitable index url at "https://pytorch-extension.intel.com/release-whl/stable/cpu/cn/" or "https://developer.intel.com/ipex-whl-stable-cpu" according to your environment.
 
 # Install other dependencies
 pip install -r requirements.txt
 ```
 
-#### 4.5 Run Baichuan2 Models with IPEX
+#### 4.2 Run Baichuan2 Models with IPEX
 
 After installed IPEX, **if the size of your Baichuan2 is 7B**, replace `modeling_baichuan.py` file under your model directory with `./baichaun2_7b_opt_ipex/modeling_baichuan.ipex`, like:
 
diff --git a/python/llm/example/CPU/Speculative-Decoding/llama2/README.md b/python/llm/example/CPU/Speculative-Decoding/llama2/README.md
index 71dfddc0..f05ac46b 100644
--- a/python/llm/example/CPU/Speculative-Decoding/llama2/README.md
+++ b/python/llm/example/CPU/Speculative-Decoding/llama2/README.md
@@ -98,27 +98,14 @@ First token latency xx.xxxxs
 
 ### 4. Accelerate with BIGDL_OPT_IPEX
 
-To accelerate speculative decoding on CPU, you can install our validated version of [IPEX 2.3.0+git0c63936](https://github.com/intel/intel-extension-for-pytorch/tree/0c63936d7a6740679987920367ae2e0cdb375b2e) by following steps: (Other versions of IPEX may have some conflicts and can not accelerate speculative decoding correctly.)
+To accelerate speculative decoding on CPU, you can install our validated version of [IPEX 2.2.0+cpu](https://github.com/intel/intel-extension-for-pytorch/tree/v2.2.0%2Bcpu) refering to [IPEX's installation guide](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=cpu&version=v2.2.0%2Bcpu), or by the following commands: (Other versions of IPEX may have some conflicts and can not accelerate speculative decoding correctly.)
 
-#### 4.1 Download IPEX installation script
 ```bash
-# Depend on Conda and GCC 12.3
-wget https://raw.githubusercontent.com/intel/intel-extension-for-pytorch/0c63936d7a6740679987920367ae2e0cdb375b2e/scripts/compile_bundle.sh
-```
-
-#### 4.2 Activate your conda environment
-```bash
-conda activate <your_conda_env>
-```
-#### 4.3 Set VER_IPEX in compile_bundle.sh to 0c63936d7a6740679987920367ae2e0cdb375b2e
-```bash
-sed -i 's/VER_IPEX=main/VER_IPEX=0c63936d7a6740679987920367ae2e0cdb375b2e/g' "compile_bundle.sh"
-```
-
-#### 4.4 Install IPEX and other dependencies
-```bash
-# Install IPEX 2.3.0+git0c63936
-bash compile_bundle.sh 
+# Install IPEX 2.2.0+cpu
+python -m pip install torch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cpu
+python -m pip install intel-extension-for-pytorch==2.2.0
+python -m pip install oneccl_bind_pt==2.2.0 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/cpu/us/
+# if there is any installation problem for oneccl_binding, you can also find suitable index url at "https://pytorch-extension.intel.com/release-whl/stable/cpu/cn/" or "https://developer.intel.com/ipex-whl-stable-cpu" according to your environment.
 
 # Update transformers
 pip install transformers==4.36.2
diff --git a/python/llm/example/CPU/Speculative-Decoding/mistral/README.md b/python/llm/example/CPU/Speculative-Decoding/mistral/README.md
index 47b31195..4556159b 100644
--- a/python/llm/example/CPU/Speculative-Decoding/mistral/README.md
+++ b/python/llm/example/CPU/Speculative-Decoding/mistral/README.md
@@ -84,4 +84,4 @@ First token latency xx.xxxxs
 
 ### 4. Accelerate with BIGDL_OPT_IPEX
 
-BIGDL_OPT_IPEX can help to accelerate speculative decoding on Mistral, and please refer to [here](https://github.com/intel-analytics/BigDL/blob/main/python/llm/example/CPU/Speculative-Decoding/baichuan2/README.md#4-accelerate-with-bigdl_opt_ipex) for a try.
+BIGDL_OPT_IPEX can help to accelerate speculative decoding on Mistral, and please refer to [here](https://github.com/intel-analytics/BigDL/blob/main/python/llm/example/CPU/Speculative-Decoding/llama2#4-accelerate-with-bigdl_opt_ipex) for a try.
diff --git a/python/llm/example/CPU/Speculative-Decoding/vicuna/README.md b/python/llm/example/CPU/Speculative-Decoding/vicuna/README.md
index 6efc11bd..b648040f 100644
--- a/python/llm/example/CPU/Speculative-Decoding/vicuna/README.md
+++ b/python/llm/example/CPU/Speculative-Decoding/vicuna/README.md
@@ -94,4 +94,4 @@ First token latency xx.xxxxs
 
 ### 4. Accelerate with BIGDL_OPT_IPEX
 
-BIGDL_OPT_IPEX can help to accelerate speculative decoding to some extend, and please refer to [here](https://github.com/intel-analytics/BigDL/blob/main/python/llm/example/CPU/Speculative-Decoding/baichuan2/README.md#4-accelerate-with-bigdl_opt_ipex) for a try.
+BIGDL_OPT_IPEX can help to accelerate speculative decoding to some extend, and please refer to [here](https://github.com/intel-analytics/BigDL/blob/main/python/llm/example/CPU/Speculative-Decoding/llama2#4-accelerate-with-bigdl_opt_ipex) for a try.
diff --git a/python/llm/src/bigdl/llm/transformers/convert.py b/python/llm/src/bigdl/llm/transformers/convert.py
index a5fd8813..824b02ba 100644
--- a/python/llm/src/bigdl/llm/transformers/convert.py
+++ b/python/llm/src/bigdl/llm/transformers/convert.py
@@ -625,27 +625,38 @@ def replace_func(m, target_m, func_name, new_func):
 def _optimize_ipex(model):
     import intel_extension_for_pytorch as ipex
     from intel_extension_for_pytorch.transformers.optimize import model_convert_reference
-    from intel_extension_for_pytorch.transformers.models.reference.models import output_hook
     from transformers.modeling_attn_mask_utils import AttentionMaskConverter
     from bigdl.llm.transformers.convert_ipex import (
-        _ipex_optimize_attention, _ipex_optimize_decoder, _ipex_jit, _make_causal_mask,
-        _ipex_optimize_rmsnorm, _llama_model_forward_4_35, convert_function, GLM_get_masks
+        _ipex_optimize_model, _ipex_jit, _make_causal_mask,
+        _llama_model_forward_4_35, convert_function, GLM_get_masks
     )
 
-    AttentionMaskConverter._make_causal_mask = _make_causal_mask
-    convert_forward(model, transformers.models.llama.modeling_llama.LlamaModel,
-                    _llama_model_forward_4_35)
     model = model_convert_reference(model)
-    if model.config.architectures is not None \
-       and model.config.architectures[0] in ["ChatGLMModel", "ChatGLMForConditionalGeneration"]:
+
+    rms_classes = [
+        transformers.models.llama.modeling_llama.LlamaRMSNorm,
+    ]
+    if 'llama' in model.config.model_type:
+        AttentionMaskConverter._make_causal_mask = _make_causal_mask
+        convert_forward(model, transformers.models.llama.modeling_llama.LlamaModel,
+                        _llama_model_forward_4_35)
+    elif "mistral" in model.config.model_type:
+        AttentionMaskConverter._make_causal_mask = _make_causal_mask
+        convert_forward(model, transformers.models.llama.modeling_llama.LlamaModel,
+                        _llama_model_forward_4_35)
+    elif model.config.architectures is not None \
+        and model.config.architectures[0] in ["ChatGLMModel", "ChatGLMForConditionalGeneration"]:  # noqa
+        # for chatglm3-6B
+        rms_classes.append(
+            type(model.transformer.encoder.layers[0].input_layernorm)
+        )
         convert_function(model.transformer, "get_masks", GLM_get_masks)
+    elif model.config.model_type == 'baichuan' and model.config.vocab_size == 125696:
+        # baichuan2
+        rms_classes.append(type(model.model.layers[0].input_layernorm))
+
     model = ipex.optimize(model.eval(), dtype=torch.bfloat16, inplace=True).eval()
-    _ipex_optimize_rmsnorm(model)
-    _ipex_optimize_attention(model)
-    _ipex_optimize_decoder(model)
-
-    model.register_forward_hook(output_hook, with_kwargs=True)
-
+    _ipex_optimize_model(model, rms_classes)
     return _ipex_jit(model)
 
 
diff --git a/python/llm/src/bigdl/llm/transformers/convert_ipex.py b/python/llm/src/bigdl/llm/transformers/convert_ipex.py
index 5ece6835..467da52b 100644
--- a/python/llm/src/bigdl/llm/transformers/convert_ipex.py
+++ b/python/llm/src/bigdl/llm/transformers/convert_ipex.py
@@ -37,59 +37,14 @@
 import torch
 from bigdl.llm.utils.common import invalidInputError
 from typing import List, Optional, Tuple, Union
+from intel_extension_for_pytorch.transformers.optimize import (
+    lowering_class_cpu,
+    convert_class,
+)
 
 
-def lowering_class_cpu(m, target_m, new_class, config, tpp=False, woq=False):
-    for name, sub_m in m.named_children():
-        if isinstance(sub_m, target_m):
-            new_m = new_class(sub_m, config, tpp, woq)
-            setattr(m, name, new_m)
-        lowering_class_cpu(sub_m, target_m, new_class, config, tpp, woq)
-
-
-def convert_class(m, target_m, new_class, config, distributed=False):
-    for name, sub_m in m.named_children():
-        if isinstance(sub_m, target_m):
-            new_m = new_class(sub_m, config, distributed)
-            setattr(m, name, new_m)
-        convert_class(sub_m, target_m, new_class, config, distributed)
-
-
-def _set_optimized_model_for_generation(
-    model,
-    optimized_model,
-    first_token_optimized_model=None,
-):
-    from intel_extension_for_pytorch.transformers.models.reference.models import (
-        IPEX_LLM_Model_Return
-    )
-    if first_token_optimized_model is not None:
-        model.trace_graph_first = IPEX_LLM_Model_Return(
-            model, first_token_optimized_model
-        ).forward
-
-    model.trace_graph = IPEX_LLM_Model_Return(model, optimized_model).forward
-    print(
-        "ipex.llm.optimize has set the optimized or quantization model for model.generate()"
-    )
-    return model
-
-
-def _ipex_optimize_rmsnorm(_model):
+def _ipex_optimize_rmsnorm(_model, supported_classes):
     from intel_extension_for_pytorch.transformers.models.cpu.fusions.mha_fusion import _IPEXRMSNorm
-    import transformers
-    supported_classes = [
-        transformers.models.llama.modeling_llama.LlamaRMSNorm,
-    ]
-    if _model.config.architectures[0] == "BaichuanForCausalLM":
-        supported_classes.append(type(_model.model.layers[0].input_layernorm))
-    if (
-        _model.config.architectures[0] == "ChatGLMModel"
-        and _model.config.rmsnorm
-    ):
-        supported_classes.append(
-            type(_model.transformer.encoder.layers[0].input_layernorm)
-        )
     for supported_class in supported_classes:
         lowering_class_cpu(
             _model,
@@ -137,8 +92,20 @@ def _ipex_optimize_attention(model):
         )
 
 
+def _ipex_optimize_model(model, rms_classes):
+    from intel_extension_for_pytorch.transformers.models.reference.models import output_hook
+
+    _ipex_optimize_rmsnorm(model, rms_classes)
+    _ipex_optimize_attention(model)
+    _ipex_optimize_decoder(model)
+    model.register_forward_hook(output_hook, with_kwargs=True)
+
+
 def _ipex_jit(model):
-    from intel_extension_for_pytorch.transformers.optimize import get_dummy_input
+    from intel_extension_for_pytorch.transformers.optimize import (
+        get_dummy_input,
+        _set_optimized_model_for_generation
+    )
     sample_inputs = (
         get_dummy_input(model, return_dict=True)
     )
@@ -158,7 +125,7 @@ def _ipex_jit(model):
             model, optimized_model=trace_model
         )
 
-    return model.eval()
+    return model
 
 
 def convert_function(m, func_name, new_function):