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Llama 2: Open Foundation and Fine-Tuned Chat Models

Authors :
Touvron, Hugo
Martin, Louis
Stone, Kevin
Albert, Peter
Almahairi, Amjad
Babaei, Yasmine
Bashlykov, Nikolay
Batra, Soumya
Bhargava, Prajjwal
Bhosale, Shruti
Bikel, Dan
Blecher, Lukas
Ferrer, Cristian Canton
Chen, Moya
Cucurull, Guillem
Esiobu, David
Fernandes, Jude
Fu, Jeremy
Fu, Wenyin
Fuller, Brian
Gao, Cynthia
Goswami, Vedanuj
Goyal, Naman
Hartshorn, Anthony
Hosseini, Saghar
Hou, Rui
Inan, Hakan
Kardas, Marcin
Kerkez, Viktor
Khabsa, Madian
Kloumann, Isabel
Korenev, Artem
Koura, Punit Singh
Lachaux, Marie-Anne
Lavril, Thibaut
Lee, Jenya
Liskovich, Diana
Lu, Yinghai
Mao, Yuning
Martinet, Xavier
Mihaylov, Todor
Mishra, Pushkar
Molybog, Igor
Nie, Yixin
Poulton, Andrew
Reizenstein, Jeremy
Rungta, Rashi
Saladi, Kalyan
Schelten, Alan
Silva, Ruan
Smith, Eric Michael
Subramanian, Ranjan
Tan, Xiaoqing Ellen
Tang, Binh
Taylor, Ross
Williams, Adina
Kuan, Jian Xiang
Xu, Puxin
Yan, Zheng
Zarov, Iliyan
Zhang, Yuchen
Fan, Angela
Kambadur, Melanie
Narang, Sharan
Rodriguez, Aurelien
Stojnic, Robert
Edunov, Sergey
Scialom, Thomas
Publication Year :
2023
Publisher :
arXiv, 2023.

Abstract

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....e4ca73730815409effc821d62f4750d1
Full Text :
https://doi.org/10.48550/arxiv.2307.09288