diff --git a/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md b/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md
index 6474d507..2785323f 100644
--- a/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md
+++ b/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md
@@ -1,11 +1,16 @@
 # AWQ
-This example shows how to directly run 4-bit AWQ models using BigDL-LLM on Intel CPU. For illustration purposes, we utilize the ["TheBloke/Llama-2-7B-Chat-AWQ"](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ) as a reference.
+This example shows how to directly run 4-bit AWQ models using BigDL-LLM on Intel CPU.
 
-## 0. Requirements
+## Verified Models
+- [Llama-2-7B-Chat-AWQ](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ)
+- [Mistral-7B-Instruct-v0.1-AWQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-AWQ)
+- [Mistral-7B-v0.1-AWQ](https://huggingface.co/TheBloke/Mistral-7B-v0.1-AWQ)
+
+## Requirements
 To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
 
 ## Example: Predict Tokens using `generate()` API
-In the example [generate.py](./generate.py), we show a basic use case for a Llama2 model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
+In the example [generate.py](./generate.py), we show a basic use case for a AWQ model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
 ### 1. Install
 We suggest using conda to manage environment:
 ```bash
@@ -13,7 +18,7 @@ conda create -n llm python=3.9
 conda activate llm
 
 pip install autoawq==0.1.6 --no-deps
-pip install bigdl-llm[all] # install bigdl-llm with 'all' option
+pip install --pre --upgrade bigdl-llm[all] # install bigdl-llm with 'all' option
 pip install transformers==4.35.0
 pip install accelerate==0.24.1
 ```
@@ -24,13 +29,13 @@ python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROM
 ```
 
 Arguments info:
-- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama2-awq model (e.g. `TheBloke/Llama-2-7B-Chat-AWQ`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'TheBloke/Llama-2-7B-Chat-AWQ'`.
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the AWQ model (e.g. `TheBloke/Llama-2-7B-Chat-AWQ`, `TheBloke/Mistral-7B-Instruct-v0.1-AWQ`, `TheBloke/Mistral-7B-v0.1-AWQ`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'TheBloke/Llama-2-7B-Chat-AWQ'`.
 - `--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`.
 
 > **Note**: When loading the model in 4-bit, BigDL-LLM converts linear layers in the model into INT4 format. In theory, a *X*B model saved in 16-bit will requires approximately 2*X* GB of memory for loading, and ~0.5*X* GB memory for further inference.
 >
-> Please select the appropriate size of the Llama2 model based on the capabilities of your machine.
+> Please select the appropriate size of the model based on the capabilities of your machine.
 
 #### 2.1 Client
 On client Windows machine, it is recommended to run directly with full utilization of all cores:
@@ -52,7 +57,7 @@ numactl -C 0-47 -m 0 python ./generate.py
 ```
 
 #### 2.3 Sample Output
-#### ["TheBloke/Llama-2-7B-Chat-AWQ"](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ)
+#### [TheBloke/Llama-2-7B-Chat-AWQ](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ)
 ```log
 Inference time: xxxx s
 -------------------- Prompt --------------------
@@ -68,4 +73,4 @@ What is AI?
 ### RESPONSE:
 
 Artificial intelligence (AI) is the ability of machines to perform tasks that typically require human intelligence, such as learning, problem-solving, decision
-```
+```
\ No newline at end of file
diff --git a/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/generate.py b/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/generate.py
index c9e7c066..42cb6ed5 100644
--- a/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/generate.py
+++ b/python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/generate.py
@@ -19,18 +19,18 @@ import time
 import argparse
 
 from bigdl.llm.transformers import AutoModelForCausalLM
-from transformers import LlamaTokenizer
+from transformers import AutoTokenizer
 
 # 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_FORMAT = """### HUMAN:
 {prompt}
 
 ### RESPONSE:
 """
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model')
+    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for AWQ model')
     parser.add_argument('--repo-id-or-model-path', type=str, default="TheBloke/Llama-2-7B-Chat-AWQ",
                         help='The huggingface repo id'
                              ', or the path to the huggingface checkpoint folder')
@@ -49,11 +49,11 @@ if __name__ == '__main__':
                                                  trust_remote_code=True)
 
     # Load tokenizer
-    tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
     
     # Generate predicted tokens
     with torch.inference_mode():
-        prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt)
+        prompt = PROMPT_FORMAT.format(prompt=args.prompt)
         input_ids = tokenizer.encode(prompt, return_tensors="pt")
         st = time.time()
         # if your selected model is capable of utilizing previous key/value attentions
diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md b/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md
index f223d5e9..52335c2d 100644
--- a/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md
+++ b/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/README.md
@@ -1,11 +1,16 @@
 # AWQ
-This example shows how to directly run 4-bit AWQ models using BigDL-LLM on Intel GPU. For illustration purposes, we utilize the ["TheBloke/Llama-2-7B-Chat-AWQ"](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ) as a reference.
+This example shows how to directly run 4-bit AWQ models using BigDL-LLM on Intel GPU.
 
-## 0. Requirements
+## Verified Models
+- [Llama-2-7B-Chat-AWQ](https://huggingface.co/TheBloke/Llama-2-7B-Chat-AWQ)
+- [Mistral-7B-Instruct-v0.1-AWQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-AWQ)
+- [Mistral-7B-v0.1-AWQ](https://huggingface.co/TheBloke/Mistral-7B-v0.1-AWQ)
+
+## Requirements
 To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
 
 ## Example: Predict Tokens using `generate()` API
-In the example [generate.py](./generate.py), we show a basic use case for a Llama2 model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
+In the example [generate.py](./generate.py), we show a basic use case for a AWQ model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
 ### 1. Install
 We suggest using conda to manage environment:
 ```bash
@@ -37,7 +42,7 @@ python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROM
 ```
 
 Arguments info:
-- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama2-awq model (e.g. `TheBloke/Llama-2-7B-Chat-AWQ`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'TheBloke/Llama-2-7B-Chat-AWQ'`.
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the AWQ model (e.g. `TheBloke/Llama-2-7B-Chat-AWQ`, `TheBloke/Mistral-7B-Instruct-v0.1-AWQ`, `TheBloke/Mistral-7B-v0.1-AWQ`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'TheBloke/Llama-2-7B-Chat-AWQ'`.
 - `--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`.
 
diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/generate.py b/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/generate.py
index 9e9b72df..136bd35d 100644
--- a/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/generate.py
+++ b/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/AWQ/generate.py
@@ -19,18 +19,18 @@ import time
 import argparse
 import intel_extension_for_pytorch as ipex
 from bigdl.llm.transformers import AutoModelForCausalLM
-from transformers import LlamaTokenizer
+from transformers import AutoTokenizer
 
 # 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_FORMAT = """### HUMAN:
 {prompt}
 
 ### RESPONSE:
 """
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Llama2 model')
+    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for AWQ model')
     parser.add_argument('--repo-id-or-model-path', type=str, default="TheBloke/Llama-2-7B-Chat-AWQ",
                         help='The huggingface repo id'
                              ', or the path to the huggingface checkpoint folder')
@@ -49,11 +49,11 @@ if __name__ == '__main__':
                                                  trust_remote_code=True,).to("xpu")
 
     # Load tokenizer
-    tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
     
     # Generate predicted tokens
     with torch.inference_mode():
-        prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt)
+        prompt = PROMPT_FORMAT.format(prompt=args.prompt)
         input_ids = tokenizer.encode(prompt, return_tensors="pt").to("xpu")
         st = time.time()
         # if your selected model is capable of utilizing previous key/value attentions
diff --git a/python/llm/src/bigdl/llm/transformers/awq/awq.py b/python/llm/src/bigdl/llm/transformers/awq/awq.py
index 511d9ede..f3112157 100644
--- a/python/llm/src/bigdl/llm/transformers/awq/awq.py
+++ b/python/llm/src/bigdl/llm/transformers/awq/awq.py
@@ -131,6 +131,8 @@ def get_blocks(model):
         layers = model.transformer.h
     elif "neox" in str(model.__class__).lower():
         layers = model.gpt_neox.layers
+    elif "mistral" in str(model.__class__).lower():
+        layers = model.model.layers
     else:
         invalidInputError(False, f"Model type {type(model)} isn't supported.")
     return layers