MiniCPM-V is a series of end-side multimodal LLMs designed for image-text understanding. These models accept image and text inputs and provide high-quality text outputs. Since February 2024, we have released four versions of the model, aiming to achieve strong performance and efficient deployment. The most noteworthy models in this series currently include:

• MiniCPM-Llama3-V 2.5: 🔥🔥🔥 The latest and most capable model in the MiniCPM-V series. With a total of 8B parameters, the model surpasses proprietary models such as GPT-4V-1106, Gemini Pro, Qwen-VL-Max and Claude 3 in overall performance. Its OCR capability and instruction-following capability have been further enhanced. The model supports multimodal interaction in over 30 languages including English, Chinese, French, Spanish, German etc. Equipped with model quantization and efficient inference technologies on CPUs, NPUs and compilation optimizations, MiniCPM-Llama3-V 2.5 can be efficiently deployed on edge devices.

• MiniCPM-V 2.0: The lightest model in the MiniCPM-V series. With 2B parameters, it surpasses larger-scale models such as Yi-VL 34B, CogVLM-Chat 17B, and Qwen-VL-Chat 10B in overall performance. It accepts image inputs of any aspect ratio up to 1.8 million pixels (e.g., 1344x1344), achieving comparable performance with Gemini Pro in understanding scene-text and matches GPT-4V in preventing hallucinations.

• [2024.05.20] We open-soure MiniCPM-Llama3-V 2.5, it has improved OCR capability and supports 30+ languages, representing the first edge-side multimodal LLM achieving GPT-4V level performance! We provide efficient inference and simple fine-tuning, try it now!
• [2024.04.23] MiniCPM-V-2.0 supports vLLM now! Click here to view more details.
• [2024.04.18] We create a HuggingFace Space to host the demo of MiniCPM-V 2.0 at here!
• [2024.04.17] MiniCPM-V-2.0 supports deploying WebUI Demo now!
• [2024.04.15] MiniCPM-V-2.0 now also supports fine-tuning with the SWIFT framework!
• [2024.04.12] We open-source MiniCPM-V-2.0, which achieves comparable performance with Gemini Pro in understanding scene text and outperforms strong Qwen-VL-Chat 9.6B and Yi-VL 34B on OpenCompass, a comprehensive evaluation over 11 popular benchmarks. Click here to view the MiniCPM-V 2.0 technical blog.
• [2024.03.14] MiniCPM-V now supports fine-tuning with the SWIFT framework. Thanks to Jintao for the contribution！
• [2024.03.01] MiniCPM-V now can be deployed on Mac!
• [2024.02.01] We open-source MiniCPM-V and OmniLMM-12B, which support efficient end-side deployment and powerful multimodal capabilities correspondingly.

• MiniCPM-Llama3-V 2.5
• MiniCPM-V 2.0
• Online Demo
• Install
• Inference
  • Model Zoo
  • Multi-turn Conversation
  • Inference on Mac
  • Deployment on Mobile Phone
  • WebUI Demo
  • Inference with vLLM
• Fine-tuning
• TODO
• Citation

MiniCPM-Llama3-V 2.5 is the latest model in the MiniCPM-V series. The model is built on SigLip-400M and Llama3-8B-Instruct with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.0. Notable features of MiniCPM-Llama3-V 2.5 include:

• 🔥 Leading Performance. MiniCPM-Llama3-V 2.5 has achieved an average score of 65.1 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks. It surpasses widely used proprietary models like GPT-4V-1106, Gemini Pro, Claude 3 and Qwen-VL-Max with 8B parameters, greatly outperforming other multimodal LLMs built on Llama 3.

• 💪 Strong OCR Capabilities. MiniCPM-Llama3-V 2.5 can process images with any aspect ratio up to 1.8 million pixels, achieving an 700+ score on OCRBench, surpassing proprietary models such as GPT-4o, GPT-4V-0409, Qwen-VL-Max and Gemini Pro. Based on recent user feedback, MiniCPM-Llama3-V 2.5 has now enhanced full-text OCR extraction, table-to-markdown conversion, and other high-utility capabilities, and has further strengthened its instruction-following and complex reasoning abilities, enhancing multimodal interaction experiences.

• 🏆 Trustworthy Behavior. Leveraging the latest RLAIF-V method (the newest technology in the RLHF-V [CVPR'24] series), MiniCPM-Llama3-V 2.5 exhibits trustworthy multimodal behavior. It achieves 10.3% hallucination rate on Object HalBench, lower than GPT-4V-1106 (13.6%), achieving the best level within the open-source community.

• 🌏 Multilingual Support. Thanks to the strong multilingual capabilities of Llama 3 and the cross-lingual generalization technique from VisCPM, MiniCPM-Llama3-V 2.5 extends its foundational bilingual (Chinese-English) multimodal capabilities to support 30+ languages including German, French, Spanish, Italian, Russian etc. We achieve this extension through only minimal instruction-tuning with translated multimodal data. All Supported Languages.

• 🚀 Efficient Deployment. MiniCPM-Llama3-V 2.5 systematically employs model quantization, CPU optimizations, NPU optimizations and compilation optimizations as acceleration techniques, achieving high-efficiency deployment on edge devices. For mobile phones with Qualcomm chips, we have integrated the NPU acceleration framework QNN into llama.cpp for the first time. After systematic optimization, MiniCPM-Llama3-V 2.5 has realized a 150-fold acceleration in multimodal large model edge-side image encoding and a 3-fold increase in language decoding speed.

Evaluation results of LLaVABench in multiple languages

We deploy MiniCPM-Llama3-V 2.5 on end devices. The demo video is the raw screen recording on a Xiaomi 14 Pro without edition.

Click here to try out the Demo of MiniCPM-Llama3-V 2.5 ｜ MiniCPM-V 2.0.

1. Clone this repository and navigate to the source folder

git clone https://github.com/OpenBMB/MiniCPM-V.git
cd MiniCPM-V

2. Create conda environment

conda create -n MiniCPM-V python=3.10 -y
conda activate MiniCPM-V

3. Install dependencies

pip install -r requirements.txt

Model	Description	Download Link
MiniCPM-Llama3-V 2.5	The lastest version, achieving state-of-the edge-side multimodal performance.	🤗
MiniCPM-Llama3-V 2.5 int4	int4 quantized version，lower GPU memory usage.	🤗
MiniCPM-V 2.0	Light version, balance the performance the computation cost.	🤗
MiniCPM-V 1.0	Lightest version, achieving the fastest inference.	🤗

Please refer to the following codes to run MiniCPM-V

from chat import OmniLMMChat, img2base64
torch.manual_seed(0)

chat_model = OmniLMMChat('openbmb/MiniCPM-Llama3-V-2_5')

im_64 = img2base64('./assets/airplane.jpeg')

# First round chat 
msgs = [{"role": "user", "content": "Tell me the model of this aircraft."}]

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": "Introduce something about Airbus A380."})

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

可以得到以下输出:

"The aircraft in the image is an Airbus A380, which can be identified by its large size, double-deck structure, and the distinctive shape of its wings and engines. The A380 is a wide-body aircraft known for being the world's largest passenger airliner, designed for long-haul flights. It has four engines, which are characteristic of large commercial aircraft. The registration number on the aircraft can also provide specific information about the model if looked up in an aviation database."

"The Airbus A380 is a double-deck, wide-body, four-engine jet airliner made by Airbus. It is the world's largest passenger airliner and is known for its long-haul capabilities. The aircraft was developed to improve efficiency and comfort for passengers traveling over long distances. It has two full-length passenger decks, which can accommodate more passengers than a typical single-aisle airplane. The A380 has been operated by airlines such as Lufthansa, Singapore Airlines, and Emirates, among others. It is widely recognized for its unique design and significant impact on the aviation industry."

# test.py  Need more than 16GB memory.
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True, low_cpu_mem_usage=True)
model = model.to(device='mps')

tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True)
model.eval()

image = Image.open('./assets/hk_OCR.jpg').convert('RGB')
question = 'Where is this photo taken?'
msgs = [{'role': 'user', 'content': question}]

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

PYTORCH_ENABLE_MPS_FALLBACK=1 python test.py

MiniCPM-V 2.0 can be deployed on mobile phones with Android operating systems. 🚀 Click here to install apk. MiniCPM-Llama3-V 2.5 coming soon.

pip install -r requirements.txt

# For NVIDIA GPUs, run:
python web_demo_2.5.py --device cuda

# For Mac with MPS (Apple silicon or AMD GPUs), run:
PYTORCH_ENABLE_MPS_FALLBACK=1 python web_demo_2.5.py --device mps

git clone https://github.com/OpenBMB/vllm.git

cd vllm
pip install -e .

pip install timm=0.9.10

python examples/minicpmv_example.py 

We supports simple fine-tuning with Hugging Face for MiniCPM-V 2.0 and MiniCPM-Llama3-V 2.5.

Reference Document

We now support finetune MiniCPM-V series with the SWIFT framework. SWIFT supports training, inference, evaluation and deployment of nearly 200 LLMs and MLLMs . It supports the lightweight training solutions provided by PEFT and a complete Adapters Library including techniques such as NEFTune, LoRA+ and LLaMA-PRO.

Best Practices：MiniCPM-V 1.0, MiniCPM-V 2.0

• MiniCPM-V fine-tuning support
• Code release for real-time interactive assistant

The code in this repo is released according to Apache-2.0

The usage of MiniCPM-V's and OmniLMM's parameters is subject to "General Model License Agreement - Source Notes - Publicity Restrictions - Commercial License"

The parameters are fully open to academic research

Please contact [email protected] to obtain written authorization for commercial uses. Free commercial use is also allowed after registration.

As LMMs, MiniCPM-V models (including OmniLMM) generate contents by learning a large amount of multimodal corpora, but they cannot comprehend, express personal opinions or make value judgement. Anything generated by MiniCPM-V models does not represent the views and positions of the model developers

We will not be liable for any problems arising from the use of MiniCPMV-V models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model.

This project is developed by the following institutions:

• THUNLP
• ModelBest
• Zhihu

👏 Welcome to explore other multimodal projects of our team:

VisCPM | RLHF-V | LLaVA-UHD | RLAIF-V

If you find your model/code/paper helpful, please consider cite the following papers:

@article{yu2023rlhf,
  title={Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback},
  author={Yu, Tianyu and Yao, Yuan and Zhang, Haoye and He, Taiwen and Han, Yifeng and Cui, Ganqu and Hu, Jinyi and Liu, Zhiyuan and Zheng, Hai-Tao and Sun, Maosong and others},
  journal={arXiv preprint arXiv:2312.00849},
  year={2023}
}
@article{viscpm,
    title={Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages}, 
    author={Jinyi Hu and Yuan Yao and Chongyi Wang and Shan Wang and Yinxu Pan and Qianyu Chen and Tianyu Yu and Hanghao Wu and Yue Zhao and Haoye Zhang and Xu Han and Yankai Lin and Jiao Xue and Dahai Li and Zhiyuan Liu and Maosong Sun},
    journal={arXiv preprint arXiv:2308.12038},
    year={2023}
}
@article{xu2024llava-uhd,
  title={{LLaVA-UHD}: an LMM Perceiving Any Aspect Ratio and High-Resolution Images},
  author={Xu, Ruyi and Yao, Yuan and Guo, Zonghao and Cui, Junbo and Ni, Zanlin and Ge, Chunjiang and Chua, Tat-Seng and Liu, Zhiyuan and Huang, Gao},
  journal={arXiv preprint arXiv:2403.11703},
  year={2024}
}