SmallThinker: A Family of Efficient Large Language Models Natively Trained for Local Deployment

Summary

This paper talks about SmallThinker, a new family of large language models (LLMs) designed from the beginning to work efficiently on local devices like laptops and smartphones instead of relying on powerful cloud computers. It uses smart engineering techniques to handle the limited computing power and memory of these everyday devices.

What's the problem?

The problem is that most large language models need lots of computer power and memory, so they usually run in big cloud data centers with expensive hardware. This makes it hard for regular users to run advanced AI on their own devices quickly and privately.

What's the solution?

SmallThinker solves this by using a special architecture that activates only important parts of the model to save computing resources, combines expert components with sparse networks to reduce workload, and preloads necessary information in the background to overcome slow device storage. It also uses memory-saving attention methods so it can run smoothly on devices like regular CPUs without needing expensive GPUs.

Why it matters?

This matters because it allows powerful AI models to be used more widely and privately by everyday people on their own devices. It makes advanced AI faster, uses less energy, and protects user privacy by removing the need to send data to the cloud, which opens up many new possibilities for AI applications.