Outperforming Giants: TinyAgent’s Edge-Based Solution Surpasses GPT-4-Turbo
Recent advancements in large language models (LLMs) have enabled the creation of sophisticated agentic systems that utilize tools and APIs to answer user queries through function calling. However, deploying these models on edge devices remains largely unexplored due to their significant size and high computational requirements, which generally necessitate cloud-based infrastructure.
In a new paper TinyAgent: Function Calling at the Edge, a research team from UC Berkeley and ICSI introduce TinyAgent, a comprehensive framework designed to train and deploy small, task-specific language models capable of performing function calls for agentic systems at the edge. Remarkably, TinyAgent outperforms larger models such as GPT-4-Turbo in this specific function-calling ability.
The research highlights that smaller models, when trained on specialized and high-quality datasets, can effectively perform complex tasks without relying on extensive world knowledge. The primary objective of this work is to develop Small Language Models (SLMs) that can be securely and privately deployed on edge devices, while still possessing the reasoning…