This article discusses advancements in language models' ability to call functions, crucial for AI agents, particularly in software applications. It details a new on-device model of 2 billion parameters designed to significantly outperform GPT-4 in terms of accuracy and latency while drastically reducing context length. This research addresses the common issues faced by existing on-device models regarding latency and accuracy, offering a solution suitable for various edge devices and real-world applications, hence making substantial contributions to the field of AI and workflow automation.
Language models have shown effectiveness in a variety of software applications, particularly in tasks related to automatic workflow. These models possess the crucial ability to call functions, which is essential in creating AI agents.
Our research presents a new method that empowers an on-device model with 2 billion parameters to surpass the performance of GPT-4 in both accuracy and latency, and decrease the context length by 95%.
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