MiniCPM 4.1 - Ultra-efficient end-side grand model introduced by Facing Face Intelligence

What is MiniCPM 4.1

MiniCPM 4.1 is an ultra-efficient end-side large language model introduced by Facade Intelligence. Adopting InfLLM v2 sparse attention architecture, each lexeme only needs to calculate the correlation with less than 5% lexeme, which significantly reduces the overhead of long text processing. In 128K long text scenarios, MiniCPM 4.1 supports efficient dual-frequency shifting mechanism, which automatically switches attention modes according to the task type, balancing computational efficiency and output accuracy. MiniCPM 4.1 has achieved the first place in multiple benchmarks for models of the same size, and its comprehensive capability has reached the best level in its class. MiniCPM 4.1 provides multiple deployment formats, such as GPTQ, AutoAWQ, etc., which is convenient for efficiently deploying it on different end-side devices.

Features of MiniCPM 4.1

• Efficient inference performance: MiniCPM 4.1 performs well on end-side devices, reasoning more than 3 times faster than open source models of the same size and responding quickly to user requests.
• Long text processing capability: Supports 128K or longer text processing, significantly reducing cache storage space compared to traditional models, making it suitable for processing long documents and complex tasks.
• hybrid thinking: It supports deep thinking and non-thinking modes, and users can choose different reasoning methods to meet diversified task requirements.
• end-user friendly: Optimized for end-side devices to reduce reliance on cloud computing and protect user privacy while reducing arithmetic and memory pressure on devices.
• Excellent overall performance: Achieved first place in the same size model on multiple evaluation benchmarks, including knowledge, reasoning, programming, and instruction following, and optimal overall ability in its class.
• Multiple deployment formatsProvides multiple deployment formats, such as GPTQ, AutoAWQ, etc., which facilitates efficient deployment on different end-side devices and adapts to a variety of application scenarios.

Core Benefits of MiniCPM 4.1

• Efficient Sparse Architecture: InfLLM v2 sparse attention mechanism is used to significantly reduce the computational complexity and memory overhead of long text processing.
• Dual-frequency gearshift mechanism: automatically switch between sparse and dense attention modes according to the task, balancing long text efficiency and short text accuracy.
• End-side optimization: Designed for end-side devices, it provides fast inference, reduces dependence on the cloud, and protects user privacy.
• Long Text Processing: Supports 128K long text processing, which significantly reduces cache storage space compared to traditional models.
• Excellent overall performance: Achieved first place in multiple evaluation benchmarks for models of the same size, with the best overall capability in its class.

What is the official website for MiniCPM 4.1

• Github repository:: https://github.com/openbmb/minicpm
• HuggingFace Model Library:: https://huggingface.co/openbmb/MiniCPM4.1-8B

People for whom MiniCPM 4.1 is available

• content creatorThe program is designed for writers, copywriters, creatives, and others who want to use its powerful text-generation capabilities as a quick way to get creative inspiration and assistance with their writing.
• Students and educators: It can be used as a learning aid to help students answer questions and organize their knowledge, and can be used as an intelligent tutoring system in the field of education.
• Developers and programmers: It excels in code generation, code completion and programming question answering, and can be used as an intelligent assistant in the development process to improve programming efficiency.
• business user: For organizations that need to deploy intelligent solutions locally, such as intelligent customer service, document processing, data analysis and other scenarios, to reduce operational costs and improve efficiency.