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).
AUTHORS
Written by
Igor Fedorov
Lemeng Wu
Tarek Elgamal
Naveen Suda
Hongyuan Zhan
Yuriy Hulovatyy
Kimish Patel
Zechun Liu
Tijmen Blankevoort
Mahesh Pasupuleti
Bilge Soran
Zacharie Delpierre Coudert
Rachad Alao
Raghuraman Krishnamoorthi
Vikas Chandra
Publisher
arXiv
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