MiniCPM-o Project Description

What is the project about?

MiniCPM-o is a series of end-side multimodal large language models (MLLMs) capable of processing images, video, text, and audio inputs, and producing high-quality text and speech outputs. It's designed for efficient deployment and strong performance, particularly on mobile devices.

What problem does it solve?

Limited multimodal capabilities on edge devices: Provides powerful multimodal understanding (vision, speech, text) and generation directly on devices like phones and tablets, reducing reliance on cloud servers.
Inefficient processing of high-resolution inputs: Offers superior token density, meaning it can process high-resolution images and videos with fewer tokens, leading to faster inference, lower latency, and reduced power consumption.
Lack of real-time multimodal interaction: Enables real-time speech conversation and multimodal live streaming, opening up new possibilities for interactive applications.
Lack of end-to-end voice cloning.

What are the features of the project?

Multimodal Input/Output: Handles images, video, text, and audio as input; generates text and speech.
GPT-4o Level Performance: Achieves performance comparable to GPT-4o in vision, speech, and multimodal live streaming.
Efficient Deployment: Designed for efficient inference on end-side devices (e.g., iPads).
High Token Density: Processes high-resolution inputs with significantly fewer tokens than competing models.
Real-time Speech Conversation: Supports bilingual (English/Chinese) real-time speech interaction with configurable voices.
Multimodal Live Streaming: Processes continuous video and audio streams, enabling real-time interaction.
Strong OCR: Excellent optical character recognition capabilities.
Trustworthy Behavior: Reduced hallucination rates compared to other models.
Multilingual Support: Supports over 30 languages.
End-to-end voice cloning.
Easy Usage: Supports various deployment methods (llama.cpp, ollama, vLLM, fine-tuning, web demos).
Multi-image and video understanding (MiniCPM-V 2.6).
In-context learning (MiniCPM-V 2.6).

What are the technologies used in the project?

Base Models: SigLip-400M, Whisper-medium-300M, ChatTTS-200M, and Qwen2.5-7B.
End-to-end Omni-modal Architecture: Connects and trains different modality encoders/decoders.
Time-division multiplexing (TDM) mechanism: For omni-modality streaming processing.
Configurable Speech Modeling Design: Uses multimodal system prompts (text and audio) for voice configuration.
RLAIF-V and VisCPM: Techniques for trustworthy behavior and multilingual support.
Deployment Frameworks: llama.cpp, ollama, vLLM, Hugging Face Transformers, Gradio.
Fine-tuning Frameworks: LLaMA-Factory, Align-Anything, SWIFT.
Quantization: int4 and GGUF quantized models.

What are the benefits of the project?

On-device AI: Brings powerful multimodal AI capabilities to edge devices, enabling offline use and reducing latency.
Improved Efficiency: Faster processing and lower power consumption due to high token density.
Enhanced User Experience: Real-time interaction and natural language processing.
New Application Possibilities: Opens up opportunities for innovative multimodal applications in areas like live streaming, accessibility, and education.
Open Source: Freely available for academic and commercial use (with registration).

What are the use cases of the project?

Real-time multimodal assistants: Interactive agents that can understand and respond to visual, auditory, and textual input.
Live streaming applications: Real-time video analysis and interaction.
Accessibility tools: Assistive technologies for visually or hearing-impaired users.
Education and training: Interactive learning experiences with multimodal content.
Content creation: Automated generation of text and speech based on visual or audio input.
Mobile applications: Enhanced capabilities for mobile devices, such as image/video understanding and voice interaction.
Voice cloning and role-playing applications.
Multilingual communication.