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MiniCPM-V 2.0

Archive at：2025-01-13

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, 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

Click to view results on TextVQA, DocVQA, OCRBench, OpenCompass, MME, MMBench, MMMU, MathVista, LLaVA Bench, Object HalBench.
Model Size TextVQA val DocVQA test OCRBench OpenCompass MME MMB dev(en) MMB dev(zh) MMMU val MathVista LLaVA Bench Object HalBench
Proprietary models
Gemini Pro Vision - 74.6 88.1 680 63.8 2148.9 75.2 74.0 48.9 45.8 79.9 -
GPT-4V - 78.0 88.4 645 63.2 1771.5 75.1 75.0 53.8 47.8 93.1 86.4 / 92.7
Open-source models 6B~34B
Yi-VL-6B 6.7B 45.5* 17.1* 290 49.3 1915.1 68.6 68.3 40.3 28.8 51.9 -
Qwen-VL-Chat 9.6B 61.5 62.6 488 52.1 1860.0 60.6 56.7 37.0 33.8 67.7 56.2 / 80.0
Yi-VL-34B 34B 43.4* 16.9* 290 52.6 2050.2 71.1 71.4 45.1 30.7 62.3 -
DeepSeek-VL-7B 7.3B 64.7* 47.0* 435 55.6 1765.4 74.1 72.8 38.3 36.8 77.8 -
TextMonkey 9.7B 64.3 66.7 558 - - - - - - - -
CogVLM-Chat 17.4B 70.4 33.3* 590 52.5 1736.6 63.7 53.8 37.3 34.7 73.9 73.6 / 87.4
Open-source models 1B~3B
DeepSeek-VL-1.3B 1.7B 58.4* 37.9* 413 46.0 1531.6 64.0 61.2 33.8 29.4 51.1 -
MobileVLM V2 3.1B 57.5 19.4* - - 1440.5(P) 63.2 - - - - -
Mini-Gemini 2.2B 56.2 34.2* - - 1653.0 59.8 - 31.7 - - -
MiniCPM-V 2.8B 60.6 38.2 366 47.6 1650.2 67.9 65.3 38.3 28.9 51.3 78.4 / 88.5
MiniCPM-V 2.0 2.8B 74.1 71.9 605 55.0 1808.6 69.6 68.1 38.2 38.7 69.2 85.5 / 92.2
* We evaluate the officially released checkpoint by ourselves.

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.

Model Zoo

Model Device Memory          Description Download
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. 🤗