diff --git a/python/llm/example/GPU/LLM-Finetuning/LISA/README.md b/python/llm/example/GPU/LLM-Finetuning/LISA/README.md
new file mode 100644
index 00000000..dfc9f1b3
--- /dev/null
+++ b/python/llm/example/GPU/LLM-Finetuning/LISA/README.md
@@ -0,0 +1,75 @@
+# LISA Finetuning with IPEX-LLM
+
+This example refers to [LISA with LMFLow's DynamicLayerActivationCallback Class](https://github.com/OptimalScale/LMFlow/blob/f3b3b007ea526009172c355e9d52ffa146b9dc0c/src/lmflow/pipeline/finetuner.py#L301), and adds [LISA fintuning](https://arxiv.org/abs/2403.17919) to IPEX-LLM on [Intel GPU](../../../GPU/README.md), based on [LORA finetuning with IPEX-LLM](../LoRA/alpaca_lora_finetuning.py).
+
+### 0. Requirements
+
+To run this example with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../GPU/README.md#requirements) for more information.
+
+### 1. Install
+
+```bash
+conda create -n llm python=3.11
+conda activate llm
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
+pip install accelerate==0.23.0
+pip install bitsandbytes==0.43.0
+pip install datasets==2.18.0
+pip install --upgrade transformers==4.36.0
+pip install scipy fire
+```
+
+### 2. LISA Finetune
+
+```bash
+# Configures OneAPI environment variables
+source /opt/intel/oneapi/setvars.sh
+python ./lisa_finetuning.py \
+    --micro_batch_size 8 \
+    --batch_size 128 \
+    --base_model "meta-llama/Llama-2-7b-hf" \
+    --data_path "yahma/alpaca-cleaned" \
+    --output_dir "./ipex-llm-lisa-alpaca" \
+    --gradient_checkpointing True \
+    --lisa_activated_layers 1 \
+    --lisa_interval_steps 20
+```
+
+Optional parameters for `lisa_finetuning.py`:
+
+**--repo-id-or-model-path** : default to `meta-llama/Llama-2-7b-hf`, and you can also specify your local model path.
+
+**--data-path** : default to `yahma/alpaca-cleaned`, and you can also specify your local datal path, while note that changing to the other datasets will introduce code modification effort for yourself.
+
+**--output-dir** : default to `./ipex-llm-lisa-alpaca` to save fine-tuned model, and you can change if needed.
+
+**--lisa_activated_layers** :  the number of self-attention layers randomly selected to activate.
+
+**lisa_interval_steps** : the number of interval steps to switch active layers.
+
+### 3. Sample Output
+
+```log
+......
+{'loss': 1.8391, 'learning_rate': 1.9967238104745695e-05, 'epoch': 0.03}                                                                                                    
+{'loss': 1.8242, 'learning_rate': 1.9869167087338908e-05, 'epoch': 0.05}                                                                                                    
+  5%|██████▉                                                        | 20/388 [xx:xx<x:xx:xx,  x.xxs/it]
+Activating layers at indices: [10] for the next steps.
+{'loss': 1.8128, 'learning_rate': 1.9706429546259592e-05, 'epoch': 0.08}                                                                                                    
+{'loss': 1.775, 'learning_rate': 1.9480091799562706e-05, 'epoch': 0.1}                                                                                                      
+ 10%|██████████████                                                  | 40/388 [xx:xx<xx:xx,  x.xxs/it]
+Activating layers at indices: [30] for the next steps.
+{'loss': 1.7669, 'learning_rate': 1.9191636897958123e-05, 'epoch': 0.13}                                                                                                    
+{'loss': 1.7749, 'learning_rate': 1.8842954907300236e-05, 'epoch': 0.15}                                                                                                    
+ 15%|█████████████████████                                           | 60/388 [xx:xx<xx:xx,  x.xxs/it]
+Activating layers at indices: [26] for the next steps.
+{'loss': 1.7735, 'learning_rate': 1.8436330524160048e-05, 'epoch': 0.18}                                                                                                    
+{'loss': 1.7199, 'learning_rate': 1.797442810562721e-05, 'epoch': 0.21}                                                                                                     
+ 21%|████████████████████████████                                    | 80/388 [xx:xx<xx:xx,  x.xxs/it]
+Activating layers at indices: [17] for the next steps.
+{'loss': 1.7328, 'learning_rate': 1.7460274211432463e-05, 'epoch': 0.23}                                                                                                    
+ 25%|█████████████████████████████████▋                             | 96/388 [xx:xx<xx:xx,  x.xxs/it]
+ ......
+
+```
diff --git a/python/llm/example/GPU/LLM-Finetuning/LISA/lisa_finetuning.py b/python/llm/example/GPU/LLM-Finetuning/LISA/lisa_finetuning.py
new file mode 100644
index 00000000..87de0ed9
--- /dev/null
+++ b/python/llm/example/GPU/LLM-Finetuning/LISA/lisa_finetuning.py
@@ -0,0 +1,167 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+from typing import List
+import fire
+import torch
+import transformers
+from datasets import load_dataset
+import accelerate
+from ipex_llm.transformers.lisa import DynamicLayerActivationCallback
+from transformers import AutoTokenizer
+
+current_dir = os.path.dirname(os.path.realpath(__file__))
+common_util_path = os.path.join(current_dir, '..')
+import sys
+sys.path.append(common_util_path)
+from common.utils import Prompter, get_train_val_data
+
+from ipex_llm.transformers import AutoModelForCausalLM
+from ipex_llm.utils.common import invalidInputError
+
+def train(
+    # model/data params
+    base_model: str = "meta-llama/Llama-2-7b-hf",  # the only required argument, default to be "meta-llama/Llama-2-7b-hf"
+    data_path: str = "yahma/alpaca-cleaned",
+    output_dir: str = "./ipex-llm-lisa-alpaca",
+    # training hyperparams
+    bf16: bool = True,  # default to bf16
+    batch_size: int = 128,
+    micro_batch_size: int = 8,  # default to be 8, limited by GPU memory
+    num_epochs: int = 1,
+    learning_rate: float = 2e-5,
+    cutoff_len: int = 256,
+    val_set_size: int = 2000,
+    # llm hyperparams
+    train_on_inputs: bool = True,  # if False, masks out inputs in loss
+    add_eos_token: bool = False,
+    group_by_length: bool = False,  # faster, but produces an odd training loss curve
+    resume_from_checkpoint: str = None,  # either training checkpoint or final adapter
+    prompt_template_name: str = "alpaca",  # The prompt template to use, will default to alpaca.
+    gradient_checkpointing: bool = False,
+    deepspeed: str = None,
+    training_mode: str = "lisa",
+    lisa_activated_layers: int = 1,
+    lisa_interval_steps: int = 20,
+):
+    invalidInputError(training_mode == "lisa",
+                      f"This example is for lisa training mode, but got training_mode={training_mode}.")
+    print(
+        f"Training Alpaca-LoRA model with params:\n"
+        f"base_model: {base_model}\n"
+        f"data_path: {data_path}\n"
+        f"output_dir: {output_dir}\n"
+        f"batch_size: {batch_size}\n"
+        f"micro_batch_size: {micro_batch_size}\n"
+        f"num_epochs: {num_epochs}\n"
+        f"learning_rate: {learning_rate}\n"
+        f"cutoff_len: {cutoff_len}\n"
+        f"val_set_size: {val_set_size}\n"
+        f"train_on_inputs: {train_on_inputs}\n"
+        f"add_eos_token: {add_eos_token}\n"
+        f"group_by_length: {group_by_length}\n"
+        f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
+        f"prompt template: {prompt_template_name}\n"
+        f"training_mode: {training_mode}\n"
+        f"lisa_activated_layers: {lisa_activated_layers}\n"
+        f"lisa_interval_steps: {lisa_interval_steps}\n"
+    )
+    assert (
+        base_model
+    ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
+    gradient_accumulation_steps = batch_size // micro_batch_size
+
+    prompter = Prompter(prompt_template_name)
+
+    model = AutoModelForCausalLM.from_pretrained(
+        base_model,
+        load_in_low_bit="bf16",
+        optimize_model=True,
+        torch_dtype=torch.bfloat16,
+        trust_remote_code=True,
+        enable_xetla=False
+    )
+
+    model = model.to("xpu")
+
+    tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
+
+    tokenizer.pad_token_id = (
+        0  # unk. we want this to be different from the eos token
+    )
+    tokenizer.padding_side = "left"  # Allow batched inference
+
+    print(model)
+
+    if data_path.endswith(".json") or data_path.endswith(".jsonl"):
+        data = load_dataset("json", data_files=data_path)
+    else:
+        data = load_dataset(data_path)
+
+    train_data, val_data = get_train_val_data(data, tokenizer, prompter, train_on_inputs,
+                                              add_eos_token, cutoff_len, val_set_size, seed=42)
+
+    trainer_callbacks = []
+
+    # Instantiate the callback
+    dynamic_layer_activation_callback = DynamicLayerActivationCallback(
+        n_layers=lisa_activated_layers,                     # Number of layers to activate
+        interval_steps = lisa_interval_steps,               # Step interval to update active layers
+        model = model
+    )
+    trainer_callbacks.append(dynamic_layer_activation_callback)
+
+    trainer = transformers.Trainer(
+        model=model,
+        train_dataset=train_data,
+        eval_dataset=val_data,
+        args=transformers.TrainingArguments(
+            per_device_train_batch_size=micro_batch_size,
+            gradient_accumulation_steps=gradient_accumulation_steps,
+            max_grad_norm=0.3,
+            num_train_epochs=num_epochs,
+            learning_rate=learning_rate,
+            lr_scheduler_type="cosine",
+            bf16=bf16,  # ensure training more stable
+            logging_steps=10,
+            optim="adamw_hf",
+            evaluation_strategy="steps" if val_set_size > 0 else "no",
+            save_strategy="steps",
+            eval_steps=200 if val_set_size > 0 else None,
+            save_steps=200,
+            output_dir=output_dir,
+            load_best_model_at_end=True if val_set_size > 0 else False,
+            group_by_length=group_by_length,
+            gradient_checkpointing=gradient_checkpointing,
+            deepspeed=deepspeed,
+            save_safetensors=False,
+        ),
+        data_collator=transformers.DataCollatorForSeq2Seq(
+            tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
+        ),
+        callbacks=trainer_callbacks
+    )
+    model.config.use_cache = False
+    
+    trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+
+    # model.save_pretrained(output_dir)
+    trainer.save_model()
+
+
+if __name__ == "__main__":
+    fire.Fire(train)
diff --git a/python/llm/src/ipex_llm/transformers/lisa.py b/python/llm/src/ipex_llm/transformers/lisa.py
new file mode 100644
index 00000000..83ca64e4
--- /dev/null
+++ b/python/llm/src/ipex_llm/transformers/lisa.py
@@ -0,0 +1,81 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from transformers import TrainerCallback
+import numpy as np
+from ipex_llm.utils.common import invalidInputError
+
+
+# source: https://github.com/OptimalScale/LMFlow/blob/main/src/lmflow/pipeline/finetuner.py
+class DynamicLayerActivationCallback(TrainerCallback):
+    def __init__(self, n_layers, interval_steps, model):
+        super().__init__()
+        self.n_layers = n_layers
+        self.interval_steps = interval_steps
+        self.model = model
+
+        # Determine the way to access layers based on the model type
+        class_to_layers_map = {
+            'LlamaForCausalLM': 'model.model.layers',
+            'Qwen2ForCausalLM': 'model.model.layers',
+            'MistralForCausalLM': 'model.model.layers',
+            'MixtralForCausalLM': 'model.model.layers',
+            'GemmaForCausalLM': 'model.model.layers',
+            'GPT2LMHeadModel': 'model.transformer.h',
+            'ChatGLMModel': 'model.transformer.encoder.layers',
+        }
+        model_class_name = self.model.__class__.__name__
+        if model_class_name in class_to_layers_map:
+            self.layers_attribute = class_to_layers_map[model_class_name]
+        else:
+            # self.layers_attribute = training_args.lisa_layers_attribute
+            invalidInputError(False, f"Model {model_class_name} not supported.")
+        # Dynamically execute to get the number of layers
+        self.total_layers = len(eval('self.' + self.layers_attribute))
+
+        self.active_layers_indices = []
+
+    def freeze_all_layers(self):
+        layers = eval('self.' + self.layers_attribute)  # Dynamically execute to get layers
+        for layer in layers:
+            for param in layer.parameters():
+                param.requires_grad = False
+
+    def on_step_begin(self, args, state, control, **kwargs):
+        # Check if it's time to switch active layers, including at step 0
+        if state.global_step % self.interval_steps == 0:
+            self.switch_active_layers()
+
+    def switch_active_layers(self):
+        # First, disable gradients for all layers
+        self.freeze_all_layers()
+
+        # Randomly select n_layers to activate
+        layers = eval('self.' + self.layers_attribute)  # Re-fetch layer references
+        self.active_layers_indices = np.random.choice(
+            range(self.total_layers),
+            self.n_layers,
+            replace=False
+        )
+        print(
+            f"Activating layers at indices: {self.active_layers_indices} for the next steps.",
+            flush=True
+        )
+
+        # Enable gradients only for the selected layers
+        for idx in self.active_layers_indices:
+            for param in layers[idx].parameters():
+                param.requires_grad = True
diff --git a/python/llm/src/ipex_llm/transformers/models/llama.py b/python/llm/src/ipex_llm/transformers/models/llama.py
index a98f02a4..8eda2212 100644
--- a/python/llm/src/ipex_llm/transformers/models/llama.py
+++ b/python/llm/src/ipex_llm/transformers/models/llama.py
@@ -313,7 +313,10 @@ def should_use_xetla_mm_qkv(self, device):
     full_attn = self.q_proj.out_len == self.k_proj.out_len == self.v_proj.out_len
     supported_qtype = self.q_proj.qtype == SYM_INT4 and full_attn
     supported_qtype = supported_qtype or self.q_proj.qtype == FP8E5
-    enable_xetla = self.q_proj.enable_xetla
+    if self.q_proj.qtype == SYM_INT4 or self.q_proj.qtype == FP8E5:
+        enable_xetla = self.q_proj.enable_xetla
+    else:
+        enable_xetla = False
     return device.type == "xpu" and enable_xetla and supported_qtype