NLP

CORE MACHINE LEARNING

Llama Guard 3-1B-INT4: Compact and Efficient Safeguard for Human-AI Conversations

November 20, 2024

Abstract

This paper presents Llama Guard 3-1B-INT4, a compact and efficient Llama Guard model, which has been open-sourced to the community during Meta Connect 2024. We demonstrate that Llama Guard 3-1B-INT4 can be deployed on resource-constrained devices, achieving a throughput of at least 30 tokens per second and a time-to-first-token of 2.5 seconds or less on a commodity Android mobile CPU. Notably, our experiments show that Llama Guard 3-1B-INT4 attains comparable or superior safety moderation scores to its larger counterpart, Llama Guard 3-1B, despite being approximately 7 times smaller in size (440MB).

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AUTHORS

Written by

Igor Fedorov

Kate Plawiak

Lemeng Wu

Tarek Elgamal

Naveen Suda

Eric Smith

Hongyuan Zhan

Jianfeng Chi

Yuriy Hulovatyy

Kimish Patel

Zechun Liu

Yangyang Shi

Tijmen Blankevoort

Mahesh Pasupuleti

Bilge Soran

Zacharie Delpierre Coudert

Rachad Alao

Raghuraman Krishnamoorthi

Vikas Chandra

Publisher

arXiv

Research Topics

Natural Language Processing (NLP)

Core Machine Learning

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