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.
Llama 2: Open Foundation and Fine-Tuned Chat Models
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.
- Year
- 2023
- Venue
- arXiv 2023
- Authors
- 68
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2307.09288v2ARXIV-DEFAULT
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68Thomas ScialomYuchen ZhangSharan NarangYinghai LuSaghar HosseiniHugo TouvronLouis MartinKevin StonePeter AlbertAmjad AlmahairiYasmine BabaeiNikolay BashlykovSoumya BatraPrajjwal BhargavaShruti BhosaleDan BikelLukas BlecherCristian Canton FerrerMoya ChenGuillem CucurullDavid EsiobuJude FernandesJeremy FuWenyin FuBrian FullerCynthia GaoVedanuj GoswamiNaman GoyalAnthony HartshornRui HouHakan InanMarcin KardasViktor KerkezMadian KhabsaIsabel KloumannArtem KorenevPunit Singh KouraMarie-Anne LachauxThibaut LavrilJenya LeeDiana LiskovichYuning MaoXavier MartinetTodor MihaylovPushkar MishraIgor MolybogYixin NieAndrew PoultonJeremy ReizensteinRashi RungtaKalyan SaladiAlan ScheltenRuan SilvaEric Michael SmithRanjan SubramanianXiaoqing Ellen TanBinh TangRoss TaylorAdina WilliamsJian Xiang KuanPuxin XuZheng YanIliyan ZarovAngela FanMelanie KambadurAurelien RodriguezRobert StojnicSergey Edunov