LLaMA: Open and Efficient Foundation Language Models

February 24, 2023


We introduce LLaMA, a collection of founda- tion language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla- 70B and PaLM-540B. We release all our models to the research community.

Download the Paper


Written by

Faisal Azhar

Hugo Touvron

Armand Joulin

Aurelien Rodriguez

Baptiste Rozière

Eric Hambro

Gautier Izacard

Guillaume Lample

Marie-Anne Lachaux

Naman Goyal

Thibaut Lavril

Timothee Lacroix

Xavier Martinet

Edouard Grave



Related Publications

December 07, 2023



Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Davide Testuggine, Madian Khabsa

December 07, 2023

December 06, 2023


Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings

Mattia Atzeni, Mike Plekhanov, Frederic Dreyer, Nora Kassner, Simone Merello, Louis Martin, Nicola Cancedda

December 06, 2023

December 04, 2023


PATHFINDER: Guided Search over Multi-Step Reasoning Paths

Olga Golovneva, Sean O'Brien, Ram Pasunuru, Tianlu Wang, Luke Zettlemoyer, Maryam Fazel-Zarandi, Asli Celikyilmaz

December 04, 2023

November 30, 2023



Efficient Monotonic Multihead Attention

Xutai Ma, Anna Sun, Siqi Ouyang, Hirofumi Inaguma, Paden Tomasello

November 30, 2023

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.