diff --git a/python/llm/example/GPU/PyTorch-Models/Model/dolly-v1/README.md b/python/llm/example/GPU/PyTorch-Models/Model/dolly-v1/README.md
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+# Dolly v1
+In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate Dolly v1 models. For illustration purposes, we utilize the [databricks/dolly-v1-6b](https://huggingface.co/databricks/dolly-v1-6b) as reference Dolly v1 models.
+
+## Requirements
+To run these examples with BigDL-LLM on Intel GPUs, 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 Dolly v1 model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations on Intel GPUs.
+### 1. Install
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://docs.conda.io/en/latest/miniconda.html#).
+
+After installing conda, create a Python environment for BigDL-LLM:
+```bash
+conda create -n llm python=3.9 # recommend to use Python 3.9
+conda activate llm
+
+# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
+# you can install specific ipex/torch version for your need
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+```
+
+### 2. Configures OneAPI environment variables
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+### 3. Run
+
+For optimal performance on Arc, it is recommended to set several environment variables.
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+```
+
+```bash
+python ./generate.py --prompt 'What is AI?'
+```
+
+In the example, several arguments can be passed to satisfy your requirements:
+
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Dolly v1 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'databricks/dolly-v1-6b'`.
+- `--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`.
+
+#### 2.3 Sample Output
+#### [databricks/dolly-v1-6b](https://huggingface.co/databricks/dolly-v1-6b)
+```log
+Inference time: xxxx s
+-------------------- Output --------------------
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+What is AI?
+
+### Response:
+AI is an umbrella term for a variety of technologies that enable computers to think and act like humans. AI can be used to automate tasks, analyze data, and
+```
diff --git a/python/llm/example/GPU/PyTorch-Models/Model/dolly-v1/generate.py b/python/llm/example/GPU/PyTorch-Models/Model/dolly-v1/generate.py
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+++ b/python/llm/example/GPU/PyTorch-Models/Model/dolly-v1/generate.py
@@ -0,0 +1,87 @@
+#
+# 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 torch
+import intel_extension_for_pytorch as ipex
+import time
+import argparse
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from bigdl.llm import optimize_model
+
+# you could tune the prompt based on your own model,
+# here the prompt tuning refers to https://huggingface.co/databricks/dolly-v1-6b#generate-text
+DOLLY_V1_PROMPT_FORMAT = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{prompt}
+
+### Response:
+"""
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Dolly v1 model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="databricks/dolly-v1-6b",
+                        help='The huggingface repo id for the Dolly v1 model to be downloaded'
+                             ', or the path to the huggingface checkpoint folder')
+    parser.add_argument('--prompt', type=str, default="What is AI?",
+                        help='Prompt to infer')
+    parser.add_argument('--n-predict', type=int, default=32,
+                        help='Max tokens to predict')
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    # Load model
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 trust_remote_code=True,
+                                                 torch_dtype='auto',
+                                                 low_cpu_mem_usage=True)
+
+    # With only one line to enable BigDL-LLM optimization on model
+    model = optimize_model(model)
+
+    model = model.to('xpu')
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    
+    # Generate predicted tokens
+    with torch.inference_mode():
+        prompt = DOLLY_V1_PROMPT_FORMAT.format(prompt=args.prompt)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
+        end_key_token_id=tokenizer.encode("### End")[0]
+        # ipex model needs a warmup, then inference time can be accurate
+        output = model.generate(input_ids,
+                                use_cache=True,
+                                max_new_tokens=args.n_predict,
+                                pad_token_id=tokenizer.pad_token_id,
+                                eos_token_id=end_key_token_id)
+
+        # start inference
+        st = time.time()
+        output = model.generate(input_ids,
+                                use_cache=True,
+                                max_new_tokens=args.n_predict,
+                                pad_token_id=tokenizer.pad_token_id,
+                                eos_token_id=end_key_token_id)
+        torch.xpu.synchronize()
+        end = time.time()
+        output = output.cpu()
+        output_str = tokenizer.decode(output[0], skip_special_tokens=False)
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Output', '-'*20)
+        print(output_str)
diff --git a/python/llm/example/GPU/PyTorch-Models/Model/dolly-v2/README.md b/python/llm/example/GPU/PyTorch-Models/Model/dolly-v2/README.md
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+++ b/python/llm/example/GPU/PyTorch-Models/Model/dolly-v2/README.md
@@ -0,0 +1,72 @@
+# Dolly v2
+In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate Dolly v2 models. For illustration purposes, we utilize the [databricks/dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b) and [databricks/dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b) as reference Dolly v2 models.
+
+## Requirements
+To run these examples with BigDL-LLM on Intel GPUs, 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 Dolly v2 model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations on Intel GPUs.
+### 1. Install
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://docs.conda.io/en/latest/miniconda.html#).
+
+After installing conda, create a Python environment for BigDL-LLM:
+```bash
+conda create -n llm python=3.9 # recommend to use Python 3.9
+conda activate llm
+
+# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
+# you can install specific ipex/torch version for your need
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+```
+
+### 2. Configures OneAPI environment variables
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+### 3. Run
+
+For optimal performance on Arc, it is recommended to set several environment variables.
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+```
+
+```bash
+python ./generate.py --prompt 'What is AI?'
+```
+
+In the example, several arguments can be passed to satisfy your requirements:
+
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Dolly v2 model (e.g. `databricks/dolly-v2-12b` and `databricks/dolly-v2-7b`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'databricks/dolly-v2-12b'`.
+- `--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`.
+
+#### 2.3 Sample Output
+
+#### [databricks/dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b)
+```log
+Inference time: xxxx s
+-------------------- Output --------------------
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+What is AI?
+
+### Response:
+Artificial Intelligence (AI) is a term generally used to describe computer systems that can perform tasks that typically require human intelligence. AI has a broad range of applications
+```
+
+#### [databricks/dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b)
+```log
+Inference time: xxxx s
+-------------------- Output --------------------
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+What is AI?
+
+### Response:
+Artificial Intelligence (AI) is a field of computer science, artificial intelligence, and robotics that focuses on understanding and mastering the principles of intelligence and making
+```
diff --git a/python/llm/example/GPU/PyTorch-Models/Model/dolly-v2/generate.py b/python/llm/example/GPU/PyTorch-Models/Model/dolly-v2/generate.py
new file mode 100644
index 00000000..7b787f02
--- /dev/null
+++ b/python/llm/example/GPU/PyTorch-Models/Model/dolly-v2/generate.py
@@ -0,0 +1,85 @@
+#
+# 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 torch
+import intel_extension_for_pytorch as ipex
+import time
+import argparse
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from bigdl.llm import optimize_model
+
+# you could tune the prompt based on your own model,
+# here the prompt tuning refers to https://huggingface.co/databricks/dolly-v2-12b/blob/main/instruct_pipeline.py#L15
+DOLLY_V2_PROMPT_FORMAT = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{prompt}
+
+### Response:
+"""
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Dolly v2 model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="databricks/dolly-v2-12b",
+                        help='The huggingface repo id for the Dolly v2 (e.g. `databricks/dolly-v2-7b` and `databricks/dolly-v2-12b`) to be downloaded'
+                             ', or the path to the huggingface checkpoint folder')
+    parser.add_argument('--prompt', type=str, default="What is AI?",
+                        help='Prompt to infer')
+    parser.add_argument('--n-predict', type=int, default=32,
+                        help='Max tokens to predict')
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    # Load model
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 trust_remote_code=True,
+                                                 torch_dtype='auto',
+                                                 low_cpu_mem_usage=True)
+
+    # With only one line to enable BigDL-LLM optimization on model
+    model = optimize_model(model)
+
+    model = model.to('xpu')
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    
+    # Generate predicted tokens
+    with torch.inference_mode():
+        prompt = DOLLY_V2_PROMPT_FORMAT.format(prompt=args.prompt)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
+        end_key_token_id=tokenizer.encode("### End")[0]
+        # ipex model needs a warmup, then inference time can be accurate
+        output = model.generate(input_ids,
+                                max_new_tokens=args.n_predict,
+                                pad_token_id=tokenizer.pad_token_id,
+                                eos_token_id=end_key_token_id)
+
+        # start inference
+        st = time.time()
+        output = model.generate(input_ids,
+                                max_new_tokens=args.n_predict,
+                                pad_token_id=tokenizer.pad_token_id,
+                                eos_token_id=end_key_token_id)
+        torch.xpu.synchronize()
+        end = time.time()
+        output = output.cpu()
+        output_str = tokenizer.decode(output[0], skip_special_tokens=False)
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Output', '-'*20)
+        print(output_str)