# Octopus v3: Technical Report for On-device Sub-billion Multimodal AI Agent

**Authors:** Wei Chen, Zhiyuan Li  
**arXiv:** [2404.11459](https://arxiv.org/abs/2404.11459)  
**Date:** 2024-04-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.11459v2/

## Key points

- The technical report introduces a novel multimodal AI agent designed by authors Wei Chen and Zhiyuan Li
- The agent can process and learn from diverse data types including natural language, visual, and audio inputs
- Its versatility allows integration of various data sources to inform actions, making it valuable for a wide range of applications
- The proposed multimodal model aims to address challenges in effectively translating image-based information into actionable outcomes for AI agents
- Optimization for constrained hardware environments ensures compatibility with devices like Raspberry Pi
- The model has less than 1 billion parameters, demonstrating efficiency on edge devices and scalability across platforms
- It can process both English and Chinese languages, expanding its applicability in diverse linguistic contexts
- Octopus v3 represents a significant advancement in on-device sub-billion parameter models, offering enhanced processing power and adaptability for handling complex data inputs across various modalities

## Summary

The technical report "Octopus v3: Technical Report for On-device Sub-billion Multimodal AI Agent" introduces a novel , designed by authors Wei Chen and Zhiyuan Li, that can process and learn from diverse data types. These include natural language, visual, and audio inputs, allowing the agent to integrate various data sources and inform its actions. This versatility makes it a valuable tool for a wide range of applications. Despite advancements in large language models like GPT-4V that incorporate visual data, effectively translating image-based information into actionable outcomes for AI agents remains challenging. To address this issue, the authors propose a multimodal model that incorporates , specifically tailored for AI agent applications. This innovative approach aims to enhance the agent's ability to interpret and respond to complex visual data efficiently. One key feature of the proposed model is its optimization for , ensuring compatibility with constrained hardware environments. With less than 1 billion parameters, the model is designed to operate efficiently on devices such as Raspberry Pi, demonstrating its versatility and scalability across different platforms. Moreover, similar to GPT-4V, the multimodal model can process both English and Chinese languages, further expanding its applicability in diverse linguistic contexts. Through their research and experimentation, the authors showcase the model's capability to perform effectively on a wide range of edge devices, highlighting its potential impact on real-world applications requiring  Overall, Octopus v3 represents a significant advancement in on-device sub-billion parameter models, offering enhanced processing power and adaptability for handling complex data inputs across various modalities. This innovative approach holds promise for advancing AI technologies in fields requiring sophisticated data processing capabilities.