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Abstract
We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. We provide multiple flavors to cover a wide range of applications: foundation models (Code Llama), Python specializations (Code Llama - Python), and instruction-following models (Code Llama - Instruct) with 7B, 13B, 34B and 70B parameters each. All models are trained on sequences of 16k tokens and show improvements on inputs with up to 100k tokens. 7B, 13B and 70B Code Llama and Code Llama - Instruct variants support infilling based on surrounding content. Code Llama reaches state-of-the-art performance among open models on several code benchmarks, with scores of up to 67% and 65% on HumanEval and MBPP, respectively. Notably, Code Llama - Python 7B outperforms Llama 2 70B on HumanEval and MBPP, and all our models outperform every other publicly available model on MultiPL-E. We release Code Llama under a permissive license that allows for both research and commercial use.
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Cite it as
@unpublished{roziere2023code,
title = {Code Llama: Open Foundation Models for Code},
author = {Baptiste Rozière and Jonas Gehring and Fabian Gloeckle and Sten Sootla and Gat, Itai and Xiaoqing Ellen Tan and Yossi Adi and Jingyu Liu and Sauvestre, Romain and Tal Remez and Jérémy Rapin and Artyom Kozhevnikov and Ivan Evtimov and Joanna Bitton and Manish Bhatt and Cristian Canton Ferrer and Aaron Grattafiori and Wenhan Xiong and Défossez, Alexandre and Jade Copet and Azhar, Faisal and Hugo Touvron and Martin, Louis and Nicolas Usunier and Thomas Scialom and Gabriel Synnaeve},
year = {2023},
journal = {arXiv (Cornell University)},
publisher = {Cornell University},
doi = {10.48550/arxiv.2308.12950},
}