MiniCPM-o-2_6
| Property | Value |
|---|---|
| Parameter Count | 8B |
| License | Apache-2.0 (code), Custom License (weights) |
| Author | openbmb |
| Model Type | Multimodal LLM |
| Architecture | End-to-end system based on SigLip-400M, Whisper-medium-300M, ChatTTS-200M, and Qwen2.5-7B |
What is MiniCPM-o-2_6?
MiniCPM-o-2_6 is a state-of-the-art multimodal language model that achieves GPT-4V level capabilities in vision, speech, and multimodal live streaming. With only 8B parameters, it outperforms many larger proprietary models in visual and audio understanding tasks while being efficient enough to run on mobile devices.
Implementation Details
The model implements an end-to-end omni-modal architecture that uniquely combines visual, audio, and text processing capabilities. It features a time-division multiplexing mechanism for handling streaming inputs and outputs, along with configurable speech modeling for voice customization.
- Achieves 70.2 average score on OpenCompass visual benchmarks
- Supports real-time speech conversation with configurable voices
- Processes images up to 1.8 million pixels with superior token density
- Implements end-to-end voice cloning capabilities
Core Capabilities
- Advanced visual understanding for images and videos
- Bilingual real-time speech conversation
- Multimodal live streaming processing
- State-of-the-art OCR performance
- Voice cloning and speech synthesis
- Efficient processing with reduced token usage
Frequently Asked Questions
Q: What makes this model unique?
MiniCPM-o-2_6 stands out for achieving GPT-4V level performance with only 8B parameters, while supporting real-time multimodal processing and voice cloning capabilities. Its efficient token density allows it to run on mobile devices while maintaining high performance.
Q: What are the recommended use cases?
The model excels in visual-audio-text applications including live video analysis, real-time speech conversation, document understanding, and voice cloning. It's particularly suitable for mobile applications requiring efficient multimodal processing.
