# MiniCPM-V-2_6
In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on MiniCPM-V-2_6 model on [Intel GPUs](../../../README.md). For illustration purposes, we utilize [openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) (or [OpenBMB/MiniCPM-V-2_6](https://www.modelscope.cn/models/OpenBMB/MiniCPM-V-2_6) for ModelScope) as reference MiniCPM-V-2_6 model.

## 0. Requirements
To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.

## Example: Predict Tokens using `chat()` API
In the example [chat.py](./chat.py), we show a basic use case for a MiniCPM-V-2_6 model to predict the next N tokens using `chat()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
### 1. Install
#### 1.1 Installation on Linux
We suggest using conda to manage environment:
```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 transformers==4.40.0 "trl<0.12.0"

# [optional] only needed if you would like to use ModelScope as model hub
pip install modelscope==1.11.0
```

#### 1.2 Installation on Windows
We suggest using conda to manage environment:
```bash
conda create -n llm python=3.11 libuv
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 transformers==4.40.0 "trl<0.12.0"

# [optional] only needed if you would like to use ModelScope as model hub
pip install modelscope==1.11.0
```

### 2. Configures OneAPI environment variables for Linux

> [!NOTE]
> Skip this step if you are running on Windows.

This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.

```bash
source /opt/intel/oneapi/setvars.sh
```

### 3. Runtime Configurations
For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
#### 3.1 Configurations for Linux
<details>

<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>

```bash
export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export SYCL_CACHE_PERSISTENT=1
```

</details>

<details>

<summary>For Intel Data Center GPU Max Series</summary>

```bash
export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export SYCL_CACHE_PERSISTENT=1
export ENABLE_SDP_FUSION=1
```
> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
</details>

<details>

<summary>For Intel iGPU</summary>

```bash
export SYCL_CACHE_PERSISTENT=1
```

</details>

#### 3.2 Configurations for Windows
<details>

<summary>For Intel iGPU and Intel Arc™ A-Series Graphics</summary>

```cmd
set SYCL_CACHE_PERSISTENT=1
```

</details>


> [!NOTE]
> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
### 4. Running examples

- chat without streaming mode:
  ```bash
  # for Hugging Face model hub
  python ./chat.py --prompt 'What is in the image?'

  # for ModelScope model hub
  python ./chat.py --prompt 'What is in the image?' --modelscope
  ```
- chat in streaming mode:
  ```bash
  # for Hugging Face model hub
  python ./chat.py --prompt 'What is in the image?' --stream

  # for ModelScope model hub
  python ./chat.py --prompt 'What is in the image?' --stream --modelscope
  ```
- save model with low-bit optimization (if `LOWBIT_MODEL_PATH` does not exist)
  ```bash
  # for Hugging Face model hub
  python ./chat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --lowbit-path LOWBIT_MODEL_PATH --prompt 'What is in the image?'

  # for ModelScope model hub
  python ./chat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --lowbit-path LOWBIT_MODEL_PATH --prompt 'What is in the image?' --modelscope
  ```
- chat with saved model with low-bit optimization (if `LOWBIT_MODEL_PATH` exists):
  ```bash
  # for Hugging Face model hub
  python ./chat.py --lowbit-path LOWBIT_MODEL_PATH --prompt 'What is in the image?'

  # for ModelScope model hub
  python ./chat.py --lowbit-path LOWBIT_MODEL_PATH --prompt 'What is in the image?' --modelscope
  ```

> [!TIP]
> For chatting in streaming mode, it is recommended to set the environment variable `PYTHONUNBUFFERED=1`.

Arguments info:
- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the **Hugging Face** or **ModelScope** repo id for the MiniCPM-V-2_6 (e.g. `openbmb/MiniCPM-V-2_6`) to be downloaded, or the path to the checkpoint folder. It is default to be `'openbmb/MiniCPM-V-2_6'` for **Hugging Face** or `'OpenBMB/MiniCPM-V-2_6'` for **ModelScope**.
- `--lowbit-path LOWBIT_MODEL_PATH`: argument defining the path to save/load the model with IPEX-LLM low-bit optimization. If it is an empty string, the original pretrained model specified by `REPO_ID_OR_MODEL_PATH` will be loaded. If it is an existing path, the saved model with low-bit optimization in `LOWBIT_MODEL_PATH` will be loaded. If it is a non-existing path, the original pretrained model specified by `REPO_ID_OR_MODEL_PATH` will be loaded, and the optimized low-bit model will be saved into `LOWBIT_MODEL_PATH`. It is default to be `''`, i.e. an empty string.
- `--image-url-or-path IMAGE_URL_OR_PATH`: argument defining the image to be infered. It is default to be `'http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg'`.
- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is in the image?'`.
- `--stream`: flag to chat in streaming mode
- `--modelscope`: using **ModelScope** as model hub instead of **Hugging Face**.

#### Sample Output

#### [openbmb/MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6)

```log
Inference time: xxxx s
-------------------- Input Image --------------------
http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
-------------------- Input Prompt --------------------
What is in the image?
-------------------- Chat Output --------------------
The image features a young child holding a white teddy bear wearing a pink dress. The background shows some red flowers and stone walls, suggesting an outdoor setting.
```
```log
-------------------- Input Image --------------------
http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
-------------------- Input Prompt --------------------
图片里有什么？
-------------------- Stream Chat Output --------------------
图片中有一个穿着粉红色连衣裙的小孩，手里拿着一只穿着粉色芭蕾裙的白色泰迪熊。背景中有红色花朵和石头墙，表明照片可能是在户外拍摄的。
```
The sample input image is (which is fetched from [COCO dataset](https://cocodataset.org/#explore?id=264959)):

<a href="http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg"><img width=400px src="http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg" ></a>