MiniCPM-V/minicpm_v1.md

MiniCPM-V 1.0

Archive at：2024-05-19

MiniCPM-V 1.0 is an efficient version with promising performance for deployment. The model is built based on SigLip-400M and MiniCPM-2.4B, connected by a perceiver resampler. Notable features of MiniCPM-V 1.0 include:

• ⚡️ High Efficiency.

  MiniCPM-V 1.0 can be efficiently deployed on most GPU cards and personal computers, and even on end devices such as mobile phones. In terms of visual encoding, we compress the image representations into 64 tokens via a perceiver resampler, which is significantly fewer than other LMMs based on MLP architecture (typically > 512 tokens). This allows MiniCPM-V 1.0 to operate with much less memory cost and higher speed during inference.

• 🔥 Promising Performance.

  MiniCPM-V 1.0 achieves state-of-the-art performance on multiple benchmarks (including MMMU, MME, and MMbech, etc) among models with comparable sizes, surpassing existing LMMs built on Phi-2. It even achieves comparable or better performance than the 9.6B Qwen-VL-Chat.

• 🙌 Bilingual Support.

  MiniCPM-V 1.0 is the first end-deployable LMM supporting bilingual multimodal interaction in English and Chinese. This is achieved by generalizing multimodal capabilities across languages, a technique from the ICLR 2024 spotlight paper.

Evaluation

Model	Size	Visual Tokens	MME	MMB dev (en)	MMB dev (zh)	MMMU val	CMMMU val
LLaVA-Phi	3B	576	1335	59.8	-	-	-
MobileVLM	3B	144	1289	59.6	-	-	-
Imp-v1	3B	576	1434	66.5	-	-	-
Qwen-VL-Chat	9.6B	256	1487	60.6	56.7	35.9	30.7
CogVLM	17.4B	1225	1438	63.7	53.8	32.1	-
MiniCPM-V 1.0	3B	64	1452	67.9	65.3	37.2	32.1

Examples

We deploy MiniCPM-V 1.0 on end devices. The demo video is the raw screen recording on a OnePlus 9R without edition.

Install

1. Clone this repository and navigate to the source folder

git clone https://github.com/OpenBMB/OmniLMM.git
cd OmniLMM

2. Create conda environment

conda create -n OmniLMM python=3.10 -y
conda activate OmniLMM

3. Install dependencies

pip install -r requirements.txt

Inference

Model Zoo

Model	Description	Download Link
MiniCPM-V 1.0	The efficient version for end device deployment.	🤗

Multi-turn Conversation

Please refer to the following codes to run MiniCPM-V 1.0.

from chat import OmniLMMChat, img2base64

chat_model = OmniLMMChat('openbmb/MiniCPM-V')

im_64 = img2base64('./assets/worldmap_ck.jpg')

# First round chat 
msgs = [{"role": "user", "content": "What is interesting about this image?"}]

inputs = {"image": im_64, "question": json.dumps(msgs)}
answer = chat_model.chat(inputs)
print(answer)

# Second round chat 
# pass history context of multi-turn conversation
msgs.append({"role": "assistant", "content": answer})
msgs.append({"role": "user", "content": "Where is China in the image"})

inputs = {"image": im_64, "question": json.dumps(msgs)}
answer = chat_model.chat(inputs)
print(answer)

Inference on Mac

Click to view example, MiniCPM-V 1.0 can run on Mac with MPS (Apple silicon or AMD GPUs).

# test.py
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained('openbmb/MiniCPM-V', trust_remote_code=True, torch_dtype=torch.bfloat16)
model = model.to(device='mps', dtype=torch.float16)

tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V', trust_remote_code=True)
model.eval()

image = Image.open('./assets/worldmap_ck.jpg').convert('RGB')
question = 'What is interesting about this image?'
msgs = [{'role': 'user', 'content': question}]

answer, context, _ = model.chat(
    image=image,
    msgs=msgs,
    context=None,
    tokenizer=tokenizer,
    sampling=True
)
print(answer)

Run with command:

PYTORCH_ENABLE_MPS_FALLBACK=1 python test.py

Deployment on Mobile Phone

Currently MiniCPM-V 1.0 can be deployed on mobile phones with Android and Harmony operating systems. 🚀 Try it out here.