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MiniCPM-Llama3-V 2.5: A GPT-4V Level Multimodal LLM on Your Phone
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**A GPT-4V Level Multimodal LLM on Your Phone** [中文](./README_zh.md) | English Join our 💬 WeChat

MiniCPM-Llama3-V 2.5 🤗 🤖 | MiniCPM-V 2.0 🤗 🤖 | Technical Blog

MiniCPM-V is a series of end-side multimodal LLMs (MLLMs) designed for vision-language understanding. The models take image and text as inputs and provide high-quality text outputs. Since February 2024, we have released 4 versions of the model, aiming to achieve strong performance and efficient deployment. The most notable models in this series currently include:

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Contents

MiniCPM-Llama3-V 2.5

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:

Evaluation

Click to view results on TextVQA, DocVQA, OCRBench, OpenCompass, MME, MMBench, MMMU, MathVista, LLaVA Bench, RealWorld QA, Object HalBench.
Model Size OCRBench TextVQA val DocVQA test Open-Compass MME MMB test (en) MMB test (cn) MMMU val Math-Vista LLaVA Bench RealWorld QA Object HalBench
Proprietary
Gemini Pro - 680 74.6 88.1 62.9 2148.9 73.6 74.3 48.9 45.8 79.9 60.4 -
GPT-4V (2023.11.06) - 645 78.0 88.4 63.5 1771.5 77.0 74.4 53.8 47.8 93.1 63.0 86.4
Open-source
Mini-Gemini 2.2B - 56.2 34.2* - 1653.0 - - 31.7 - - - -
Qwen-VL-Chat 9.6B 488 61.5 62.6 51.6 1860.0 61.8 56.3 37.0 33.8 67.7 49.3 56.2
DeepSeek-VL-7B 7.3B 435 64.7* 47.0* 54.6 1765.4 73.8 71.4 38.3 36.8 77.8 54.2 -
Yi-VL-34B 34B 290 43.4* 16.9* 52.2 2050.2 72.4 70.7 45.1 30.7 62.3 54.8 79.3
CogVLM-Chat 17.4B 590 70.4 33.3* 54.2 1736.6 65.8 55.9 37.3 34.7 73.9 60.3 73.6
TextMonkey 9.7B 558 64.3 66.7 - - - - - - - - -
Idefics2 8.0B - 73.0 74.0 57.2 1847.6 75.7 68.6 45.2 52.2 49.1 60.7 -
Bunny-LLama-3-8B 8.4B - - - 54.3 1920.3 77.0 73.9 41.3 31.5 61.2 58.8 -
LLaVA-NeXT Llama-3-8B 8.4B - - 78.2 - 1971.5 - - 41.7 37.5 80.1 60.0 -
Phi-3-vision-128k-instruct 4.2B 639* 70.9 - - 1537.5* - - 40.4 44.5 64.2* 58.8* -
MiniCPM-V 1.0 2.8B 366 60.6 38.2 47.5 1650.2 64.1 62.6 38.3 28.9 51.3 51.2 78.4
MiniCPM-V 2.0 2.8B 605 74.1 71.9 54.5 1808.6 69.1 66.5 38.2 38.7 69.2 55.8 85.5
MiniCPM-Llama3-V 2.5 8.5B 725 76.6 84.8 65.1 2024.6 77.2 74.2 45.8 54.3 86.7 63.5 89.7
* We evaluate the officially released checkpoint by ourselves.

Evaluation results of multilingual LLaVA Bench

Examples

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.

MiniCPM-V 2.0

Click to view more details of MiniCPM-V 2.0 **MiniCPM-V 2.0** is an efficient version with promising performance for deployment. The model is built based on SigLip-400M and [MiniCPM-2.4B](https://github.com/OpenBMB/MiniCPM/), connected by a perceiver resampler. Our latest version, MiniCPM-V 2.0 has several notable features. - 🔥 **State-of-the-art Performance.** MiniCPM-V 2.0 achieves **state-of-the-art performance** on multiple benchmarks (including OCRBench, TextVQA, MME, MMB, MathVista, etc) among models under 7B parameters. It even **outperforms strong Qwen-VL-Chat 9.6B, CogVLM-Chat 17.4B, and Yi-VL 34B on OpenCompass, a comprehensive evaluation over 11 popular benchmarks**. Notably, MiniCPM-V 2.0 shows **strong OCR capability**, achieving **comparable performance to Gemini Pro in scene-text understanding**, and **state-of-the-art performance on OCRBench** among open-source models. - 🏆 **Trustworthy Behavior.** LMMs are known for suffering from hallucination, often generating text not factually grounded in images. MiniCPM-V 2.0 is **the first end-side LMM aligned via multimodal RLHF for trustworthy behavior** (using the recent [RLHF-V](https://rlhf-v.github.io/) [CVPR'24] series technique). This allows the model to **match GPT-4V in preventing hallucinations** on Object HalBench. - 🌟 **High-Resolution Images at Any Aspect Raito.** MiniCPM-V 2.0 can accept **1.8 million pixels (e.g., 1344x1344) images at any aspect ratio**. This enables better perception of fine-grained visual information such as small objects and optical characters, which is achieved via a recent technique from [LLaVA-UHD](https://arxiv.org/pdf/2403.11703.pdf). - ⚡️ **High Efficiency.** MiniCPM-V 2.0 can be **efficiently deployed on most GPU cards and personal computers**, and **even on end devices such as mobile phones**. For visual encoding, we compress the image representations into much fewer tokens via a perceiver resampler. This allows MiniCPM-V 2.0 to operate with **favorable memory cost and speed during inference even when dealing with high-resolution images**. - 🙌 **Bilingual Support.** MiniCPM-V 2.0 **supports strong bilingual multimodal capabilities in both English and Chinese**. This is enabled by generalizing multimodal capabilities across languages, a technique from [VisCPM](https://arxiv.org/abs/2308.12038) [ICLR'24]. ### Examples

We deploy MiniCPM-V 2.0 on end devices. The demo video is the raw screen recording on a Xiaomi 14 Pro without edition.

Legacy Models

Model Introduction and Guidance
MiniCPM-V 1.0 Document
OmniLMM-12B Document

Chat with Our Demo on Gradio

We provide online and local demos powered by HuggingFace Gradio, the most popular model deployment framework nowadays. It supports streaming outputs, progress bars, queuing, alerts, and other useful features.

Online Demo

Click here to try out the online demo of MiniCPM-Llama3-V 2.5 ｜ MiniCPM-V 2.0 on HuggingFace Spaces.

Local WebUI Demo

You can easily build your own local WebUI demo with Gradio using the following commands.

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

Install

  1. Clone this repository and navigate to the source folder
git clone https://github.com/OpenBMB/MiniCPM-V.git
cd MiniCPM-V
  1. Create conda environment
conda create -n MiniCPM-V python=3.10 -y
conda activate MiniCPM-V
  1. Install dependencies
pip install -r requirements.txt

Inference

Model Zoo

Model Device Memory          Description Download
MiniCPM-Llama3-V 2.5 GPU 19 GB The lastest version, achieving state-of-the end-side multimodal performance. 🤗   
MiniCPM-Llama3-V 2.5 gguf CPU 5 GB The gguf version, lower memory usage and faster inference. 🤗   
MiniCPM-Llama3-V 2.5 int4 GPU 8 GB The int4 quantized version，lower GPU memory usage. 🤗   
MiniCPM-V 2.0 GPU 8 GB Light version, balance the performance the computation cost. 🤗   
MiniCPM-V 1.0 GPU 7 GB Lightest version, achieving the fastest inference. 🤗   

Multi-turn Conversation

Please refer to the following codes to run.

from chat import MiniCPMVChat, img2base64
import torch
import json

torch.manual_seed(0)

chat_model = MiniCPMVChat('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)

You will get the following output:

"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."

Inference on Multiple GPUs

You can run MiniCPM-Llama3-V 2.5 on multiple low VRAM GPUs (12 GB or 16 GB) by distributing the model's layers across multiple GPUs. Please refer to this tutorial for detailed instructions on how to load the model and inference using multiple low VRAM GPUs.

Inference on Mac

Click to view an example, to run MiniCPM-Llama3-V 2.5 on 💻 Mac with MPS (Apple silicon or AMD GPUs). ```python # 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) ``` Run with command: ```shell PYTORCH_ENABLE_MPS_FALLBACK=1 python test.py ```

Deployment on Mobile Phone

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

Inference with llama.cpp

MiniCPM-Llama3-V 2.5 can run with llama.cpp now! See our fork of llama.cpp for more detail. This implementation supports smooth inference of 6~8 token/s on mobile phones (test environment：Xiaomi 14 pro + Snapdragon 8 Gen 3).

Inference with vLLM

Click to see how to inference MiniCPM-V 2.0 with vLLM (MiniCPM-Llama3-V 2.5 coming soon) Because our pull request to vLLM is still waiting for reviewing, we fork this repository to build and test our vLLM demo. Here are the steps: 1. Clone our version of vLLM: ```shell git clone https://github.com/OpenBMB/vllm.git ``` 2. Install vLLM: ```shell cd vllm pip install -e . ``` 3. Install timm: ```shell pip install timm==0.9.10 ``` 4. Run our demo: ```shell python examples/minicpmv_example.py ```

Fine-tuning

Simple Fine-tuning

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

Reference Document

With the SWIFT Framework

We now support MiniCPM-V series fine-tuning 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

TODO

Model License

Statement

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.

Institutions

This project is developed by the following institutions:

Other Multimodal Projects from Our Team

👏 Welcome to explore other multimodal projects of our team:

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

🌟 Star History

Citation

If you find our model/code/paper helpful, please consider cite our papers 📝 and star us ⭐️！

@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}
}
@article{yu2024rlaifv,
  title={RLAIF-V: Aligning MLLMs through Open-Source AI Feedback for Super GPT-4V Trustworthiness}, 
  author={Yu, Tianyu and Zhang, Haoye and Yao, Yuan and Dang, Yunkai and Chen, Da and Lu, Xiaoman and Cui, Ganqu and He, Taiwen and Liu, Zhiyuan and Chua, Tat-Seng and Sun, Maosong},
  journal={arXiv preprint arXiv:2405.17220},
  year={2024}
}