VinaLLaMA: LLaMA-based Vietnamese Foundation Model

Autor:	Nguyen, Quan, Pham, Huy, Dao, Dung
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	In this technical report, we present VinaLLaMA, an open-weight, state-of-the-art (SOTA) Large Language Model for the Vietnamese language, built upon LLaMA-2 with an additional 800 billion trained tokens. VinaLLaMA not only demonstrates fluency in Vietnamese but also exhibits a profound understanding of Vietnamese culture, making it a truly indigenous model. VinaLLaMA-7B-chat, trained on 1 million high-quality synthetic samples, achieves SOTA results on key benchmarks, including VLSP, VMLU, and Vicuna Benchmark Vietnamese, marking a significant advancement in the Vietnamese AI landscape and offering a versatile resource for various applications. Comment: VinaLLaMA Technical Report - 13 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.11011 Zobrazit plný text záznamu View this record from Arxiv