What is MiniCPM 3.0?

MiniCPM 3.0 is a high-performance edge AI model developed by FaceWall Intelligence, featuring 4B parameters. Despite its smaller size, it surpasses GPT-3.5 in performance. The model utilizes LLMxMapReduce technology to support infinite-length text processing, effectively expanding its contextual understanding capabilities. In Function Calling, MiniCPM 3.0 performs close to GPT-4o, demonstrating excellent edge-side execution capabilities. The model also includes the RAG trio (retrieval, re-ranking, and generation models), significantly improving Chinese retrieval and content generation quality. MiniCPM 3.0 is fully open-source, with the quantized model occupying only 2GB of memory, making it ideal for edge-side deployment while ensuring data security and privacy.

Key Features of MiniCPM 3.0

• Surpassing Performance: Despite having only 4B parameters, it outperforms GPT-3.5, showcasing strong language processing capabilities.
• Infinite-Length Text Processing: Utilizes LLMxMapReduce technology to support infinite-length text input, breaking the context length limitations of traditional large models.
• Edge-Side Optimization: The quantized model occupies only 2GB of memory, making it ideal for running on edge-side devices like smartphones and tablets.
• Function Calling: Implements powerful Function Calling capabilities on edge-side devices, enabling the understanding and execution of complex user instructions.
• RAG Trio: Includes MiniCPM-Embedding (retrieval model), MiniCPM-Reranker (re-ranking model), and LoRA plugin (generation model), providing efficient information retrieval and content generation.
• Open-Source Model: The model code and weights are open-source, allowing the community to freely use and further develop it.
• Security and Privacy Protection: As an edge-side model, MiniCPM 3.0 processes data locally, better protecting user privacy and data security.
• Multi-Task Performance: Demonstrates excellent performance in tasks such as open-domain question answering, multi-hop question answering, dialogue systems, fact-checking, and information filling.

Technical Principles of MiniCPM 3.0

• LLMxMapReduce Technology: A long-text framing processing technology that allows the model to process text beyond its original memory limits. By splitting long text into small chunks (or "frames"), the model can process the entire text piece by piece, enabling "infinite" long-text processing.
• Quantization Technology: MiniCPM 3.0 employs quantization technology to reduce the model's memory requirement to 2GB, allowing it to run on resource-constrained edge-side devices without sacrificing much performance.
• Function Calling: A technology that enables the model to understand and execute user instructions, involving calling external applications or services. MiniCPM 3.0's performance in this area is close to GPT-4o, showcasing its potential to execute complex tasks on edge-side devices.
• RAG (Retrieval-Augmented Generation): A technology that combines retrieval and generation, allowing the model to retrieve relevant information from large datasets and use it to generate more accurate and rich responses. MiniCPM 3.0's RAG trio includes:
• MiniCPM-Embedding: A model for retrieval tasks, capable of efficiently finding relevant information from large datasets.
• MiniCPM-Reranker: Re-ranks the retrieved candidate answers to improve relevance and accuracy.
• LoRA Plugin: A generation model optimized for RAG scenarios, capable of generating coherent and accurate text from retrieved information.
• Model Fine-Tuning: MiniCPM 3.0 supports fine-tuning for specific tasks, adapting to different application scenarios and needs. This involves further training the model on specific datasets to improve its performance on those tasks.
• Efficient Training Methods: FaceWall Intelligence employs scientific training methods and data quality control to enhance the model's "knowledge density," i.e., the ratio of model capability to its parameter count. This helps improve the model's performance while keeping its size unchanged.

Project Address of MiniCPM 3.0

• GitHub Repository: https://github.com/OpenBMB/MiniCPM
• HuggingFace Model Library: https://huggingface.co/openbmb/MiniCPM3-4B

Application Scenarios of MiniCPM 3.0

• Smart Assistants: As a smart assistant for individual or enterprise users, MiniCPM 3.0 can handle and respond to various queries, providing services like schedule management, information retrieval, and email processing.
• Mobile Device Applications: Due to the model's edge-side optimization and quantization technology, MiniCPM 3.0 is ideal for integration into smartphones, tablets, and other mobile devices, offering instant AI services.
• Smart Home Control: In smart home systems, MiniCPM 3.0 can serve as the central processing unit, understanding and executing user voice commands to control various smart devices at home.
• Online Customer Service: In the customer service field, MiniCPM 3.0 can provide 24/7 automated response services, handling common questions and user inquiries.
• Content Creation and Editing: MiniCPM 3.0's text generation capabilities can assist creators in writing articles, generating reports, or editing text, improving creative efficiency.