MiniCPM V 2

---
pipeline_tag: visual-question-answering
language:

• en

• zh

• HaoyeZhang/RLHF-V-Dataset

• Yirany/UniMM-Chat

• HuggingFaceM4/VQAv2

• liuhaotian/LLaVA-Instruct-150K

GitHub | Demo

News <!-- omit in toc -->

• [2025.01.14] 🔥 We open source MiniCPM-o 2.6, with significant performance improvement over MiniCPM-V 2.6, and support real-time speech-to-speech conversation and multimodal live streaming. Try it now.

• [2024.08.06] 🔥 We open-source MiniCPM-V 2.6, which outperforms GPT-4V on single image, multi-image and video understanding. It advances popular features of MiniCPM-Llama3-V 2.5, and can support real-time video understanding on iPad.

• [2024.05.20] 🔥 The GPT-4V level multimodal model MiniCPM-Llama3-V 2.5 is out.

• [2024.04.23] MiniCPM-V 2.0 supports vLLM now!

• [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 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 <a href="https://rank.opencompass.org.cn/leaderboard-multimodal">OpenCompass</a>, a comprehensive evaluation over 11 popular benchmarks. Click <a href="https://openbmb.vercel.app/minicpm-v-2">here</a> to view the MiniCPM-V 2.0 technical blog.

MiniCPM-V 2.0

MiniCPM-V 2.8B is a strong multimodal large language model for efficient end-side deployment. The model is built based on SigLip-400M and MiniCPM-2.4B, 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 [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.

• ⚡️ 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 [ICLR'24].

Evaluation <!-- omit in toc -->

<div align="center">
<img src=/openbmb/MiniCPM-V-2.0/resolve/main/assets/minicpmv-2-peformance2.png width=100% />
</div>
Results on TextVQA, DocVQA, OCRBench, OpenCompass, MME, MMBench, MMMU, MathVista, LLaVA Bench, Object HalBench.
<div align="center">
<img src=/openbmb/MiniCPM-V-2.0/resolve/main/assets/minicpmv-2-benchmark.png width=140% />
</div>

Examples <!-- omit in toc -->

<table align="center">
<p align="center">
<img src="assets/minicpmv2-cases_2.png" width=95%/>
</p>
</table>

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

<table align="center">
<p align="center">
<img src="assets/station.gif" width=40% style="display:inline-block;"/>
<img src="assets/london_car.gif" width=40% style="display:inline-block;"/>
</p>
</table>

Demo

Deployment on Mobile Phone

Inference with vLLM<a id="vllm"></a>

<details>
<summary>Click to see how to inference with vLLM </summary>
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:

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

cd vllm
pip install -e .

pip install timm=0.9.10

python examples/minicpmv_example.py

Usage

Pillow==10.1.0
timm==0.9.10
torch==2.1.2
torchvision==0.16.2
transformers==4.36.0
sentencepiece==0.1.99

# test.py
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2', trust_remote_code=True, torch_dtype=torch.bfloat16)
For Nvidia GPUs support BF16 (like A100, H100, RTX3090)

model = model.to(device='cuda', dtype=torch.bfloat16)
For Nvidia GPUs do NOT support BF16 (like V100, T4, RTX2080)

#model = model.to(device='cuda', dtype=torch.float16)
For Mac with MPS (Apple silicon or AMD GPUs).

Run with PYTORCH_ENABLE_MPS_FALLBACK=1 python test.py

#model = model.to(device='mps', dtype=torch.float16)

tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2', trust_remote_code=True)
model.eval()
image = Image.open('xx.jpg').convert('RGB')
question = 'What is in the image?'
msgs = [{'role': 'user', 'content': question}]
res, context, _ = model.chat(
    image=image,
    msgs=msgs,
    context=None,
    tokenizer=tokenizer,
    sampling=True,
    temperature=0.7
)
print(res)

Please look at GitHub for more detail about usage.

MiniCPM-V 1.0 <!-- omit in toc -->

License

• The code in this repo is released under the Apache-2.0 License.

• The usage of MiniCPM-V series model weights must strictly follow MiniCPM Model License.md.

• The models and weights of MiniCPM are completely free for academic research. after filling out a ["questionnaire"](https://m