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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 models. For illustration purposes, we utilize the openbmb/MiniCPM-V-2_6 as a reference MiniCPM-V-2_6 model.

0. Requirements

To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to here for more information.

Example: Predict Tokens using chat() API

In the example 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.

1. Install

We suggest using conda to manage environment:

On Linux:

conda create -n llm python=3.11
conda activate llm

# install ipex-llm with 'all' option
pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
pip install torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cpu
pip install transformers==4.40.0 trl

On Windows:

conda create -n llm python=3.11
conda activate llm

pip install --pre --upgrade ipex-llm[all]
pip install torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cpu
pip install transformers==4.41.0 trl

2. Run

  • chat without streaming mode: 
    python ./chat.py --prompt 'What is in the image?'
  • chat in streaming mode: 
    python ./chat.py --prompt 'What is in the image?' --stream

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 huggingface repo id for the MiniCPM-V-2_6 model (e.g. openbmb/MiniCPM-V-2_6) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'openbmb/MiniCPM-V-2_6'.
  • --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

Note: When loading the model in 4-bit, IPEX-LLM converts linear layers in the model into INT4 format. In theory, a XB model saved in 16-bit will requires approximately 2X GB of memory for loading, and ~0.5X GB memory for further inference.

Please select the appropriate size of the MiniCPM 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:

python ./chat.py

2.2 Server

For optimal performance on server, it is recommended to set several environment variables (refer to here for more information), and run the example with all the physical cores of a single socket.

E.g. on Linux,

# set IPEX-LLM env variables
source ipex-llm-init

# e.g. for a server with 48 cores per socket
export OMP_NUM_THREADS=48
numactl -C 0-47 -m 0 python ./chat.py

2.3 Sample Output

openbmb/MiniCPM-V-2_6

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 dressed in pink. The background includes some red flowers and what appears to be a stone wall.

-------------------- Input Image --------------------
http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg
-------------------- Input Prompt --------------------
图片里有什么？
-------------------- Stream Chat Output --------------------
图片中有一个小女孩，她手里拿着一个穿着粉色裙子的白色小熊玩偶。背景中有红色花朵和石头结构，可能是一个花园或庭院。

The sample input image is (which is fetched from COCO dataset):