| Property | Value |
|---|---|
| Parameter Count | 8B |
| Architecture | SigLip-400M + Qwen2-7B |
| License | Apache-2.0 (code) + Custom License (weights) |
| Author | openbmb |
| Model URL | https://huggingface.co/openbmb/MiniCPM-V-2_6 |
What is MiniCPM-V-2_6?
MiniCPM-V-2_6 is a state-of-the-art multimodal language model that achieves GPT-4V level performance while maintaining exceptional efficiency. Built on SigLip-400M and Qwen2-7B architectures, it represents a significant advancement in multimodal AI, capable of understanding single images, multiple images, and videos with remarkable accuracy.
Implementation Details
The model leverages an efficient architecture that produces only 640 tokens when processing a 1.8M pixel image, resulting in 75% fewer tokens than comparable models. It supports various deployment options including llama.cpp, ollama, and offers int4 quantization for reduced memory usage.
- Supports images up to 1.8 million pixels (1344x1344)
- Features state-of-the-art token density for efficient processing
- Implements advanced OCR capabilities surpassing GPT-4V
- Provides multilingual support across English, Chinese, German, French, Italian, Korean
Core Capabilities
- Single image understanding with 65.2 average score on OpenCompass
- Multi-image reasoning and comparison
- Video understanding with dense caption generation
- Strong OCR performance exceeding proprietary models
- Real-time video processing on end devices like iPad
Frequently Asked Questions
Q: What makes this model unique?
The model's exceptional efficiency in token generation and ability to process multiple types of visual inputs (single images, multiple images, and videos) while maintaining GPT-4V level performance makes it stand out. Its ability to run on end devices with optimized performance is particularly notable.
Q: What are the recommended use cases?
The model excels in image and video analysis, OCR tasks, multilingual visual understanding, and real-time video processing. It's particularly suitable for applications requiring efficient processing on end devices or when dealing with multiple visual inputs simultaneously.