Citation

Llama 2: Open Foundation and Fine-Tuned Chat Models

Author:
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
Year:
2023

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.