diff --git a/python/llm/example/CPU/Applications/hf-agent/README.md b/python/llm/example/CPU/Applications/hf-agent/README.md
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+# BigDL-LLM Transformers INT4 Optimization for HuggingFace Transformers Agent
+In this example, we apply low-bit optimizations to [HuggingFace Transformers Agents](https://huggingface.co/docs/transformers/transformers_agents) using BigDL-LLM, which allows LLMs to use tools such as image generation, image captioning, text summarization, etc.
+
+For illustration purposes, we utilize the [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) as the reference model. We use [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) to create an agent, and then ask the agent to generate the caption for an image from coco dataset, i.e. [demo.jpg](https://cocodataset.org/#explore?id=264959)
+
+## 0. Requirements
+To run this example with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model#recommended-requirements) for more information.
+
+
+### 1. Install
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.9
+conda activate llm
+
+pip install bigdl-llm[all] # install bigdl-llm with 'all' option
+pip install pillow # additional package required for opening images
+```
+
+### 2. Run
+```
+python ./run_agent.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --image-path IMAGE_PATH
+```
+
+Arguments info:
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Vicuna model (e.g. `lmsys/vicuna-7b-v1.5`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'lmsys/vicuna-7b-v1.5'`.
+- `--image-path IMAGE_PATH`: argument defining the image to be infered. It is default to be `demo.jpg`.
+
+> **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 Vicuna 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:
+```powershell
+python ./run_agent.py
+```
+
+#### 2.2 Server
+For optimal performance on server, it is recommended to set several environment variables (refer to [here](../README.md#best-known-configuration-on-linux) for more information), and run the example with all the physical cores of a single socket.
+
+E.g. on Linux,
+```bash
+# set BigDL-Nano env variables
+source bigdl-nano-init
+
+# e.g. for a server with 48 cores per socket
+export OMP_NUM_THREADS=48
+numactl -C 0-47 -m 0 python ./run_agent.py
+```
+
+#### 2.3 Sample Output
+#### [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5)
+```log
+Image path: demo.jpg
+== Prompt ==
+Generate a caption for the 'image'
+==Explanation from the agent==
+I will use the following tool: `image_captioner` to generate a caption for the image.
+
+
+==Code generated by the agent==
+caption = image_captioner(image)
+
+
+==Result==
+a little girl holding a stuffed teddy bear
+```
diff --git a/python/llm/example/CPU/Applications/hf-agent/demo.jpg b/python/llm/example/CPU/Applications/hf-agent/demo.jpg
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diff --git a/python/llm/example/CPU/Applications/hf-agent/run_agent.py b/python/llm/example/CPU/Applications/hf-agent/run_agent.py
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+#
+# 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 argparse
+
+from PIL import Image
+from transformers import AutoTokenizer, LocalAgent
+
+from bigdl.llm.transformers import AutoModelForCausalLM
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run agent using vicuna model")
+    parser.add_argument("--repo-id-or-model-path", type=str, default="lmsys/vicuna-7b-v1.5",
+                        help="The huggingface repo id for the Vicuna model to be downloaded"
+                             ", or the path to the huggingface checkpoint folder")
+    parser.add_argument("--image-path", type=str, default="demo.jpg",
+                        help="Image to generate caption")
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    # Load model in 4 bit,
+    # which convert the relevant layers in the model into INT4 format
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 load_in_4bit=True)
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    # Load image
+    image = Image.open(args.image_path)
+    # Create an agent
+    agent = LocalAgent(model, tokenizer)
+
+    # Generate results
+    prompt = "Generate a caption for the 'image'"
+    print(f"Image path: {args.image_path}")
+    print('==', 'Prompt', '==')
+    print(prompt)
+    print(agent.run(prompt, image=image))
diff --git a/python/llm/example/CPU/applications/streaming-llm/README.md b/python/llm/example/CPU/Applications/streaming-llm/README.md
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diff --git a/python/llm/example/CPU/applications/streaming-llm/streaming_llm/__init__.py b/python/llm/example/CPU/Applications/streaming-llm/streaming_llm/__init__.py
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diff --git a/python/llm/example/CPU/applications/streaming-llm/streaming_llm/enable_streaming_llm.py b/python/llm/example/CPU/Applications/streaming-llm/streaming_llm/enable_streaming_llm.py
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diff --git a/python/llm/example/CPU/applications/streaming-llm/streaming_llm/kv_cache.py b/python/llm/example/CPU/Applications/streaming-llm/streaming_llm/kv_cache.py
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diff --git a/python/llm/example/CPU/applications/streaming-llm/streaming_llm/modify_falcon.py b/python/llm/example/CPU/Applications/streaming-llm/streaming_llm/modify_falcon.py
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diff --git a/python/llm/example/CPU/applications/streaming-llm/streaming_llm/modify_gpt_neox.py b/python/llm/example/CPU/Applications/streaming-llm/streaming_llm/modify_gpt_neox.py
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diff --git a/python/llm/example/CPU/applications/streaming-llm/streaming_llm/modify_llama.py b/python/llm/example/CPU/Applications/streaming-llm/streaming_llm/modify_llama.py
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diff --git a/python/llm/example/CPU/applications/streaming-llm/streaming_llm/utils.py b/python/llm/example/CPU/Applications/streaming-llm/streaming_llm/utils.py
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diff --git a/python/llm/example/CPU/README.md b/python/llm/example/CPU/README.md
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@@ -6,6 +6,7 @@ This folder contains examples of running BigDL-LLM on Intel CPU:
 - [PyTorch-Models](PyTorch-Models): running any PyTorch model on BigDL-LLM (with "one-line code change")
 - [Native-Models](Native-Models): converting & running LLM in `llama`/`chatglm`/`bloom`/`gptneox`/`starcoder` model family using native (cpp) implementation
 - [LangChain](LangChain): running LangChain applications on BigDL-LLM
+- [Applications](Applications): running Transformers applications on BigDl-LLM
 
 ## System Support
 **Hardware**: