有用性
计算机科学
工作(物理)
基础(证据)
比例(比率)
竞赛(生物学)
心理学
工程类
生态学
地理
考古
社会心理学
生物
地图学
机械工程
作者
Hugo Touvron,Louis Martin,Kevin H. Stone,Peter J. Albert,Amjad Almahairi,Yasmine Babaei,Nikolay Bashlykov,Soumya Batra,Prajjwal Bhargava,Shruti Bhosale,Dan Bikel,Lukas Blecher,Cristian Canton Ferrer,Moya Chen,Guillem Cucurull,David Esiobu,Jude Fernandes,Jeremy Fu,Wenyin Fu,Brian Fuller
出处
期刊:Cornell University - arXiv
日期:2023-07-18
被引量:2581
标识
DOI:10.48550/arxiv.2307.09288
摘要
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.
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