StarCoder: may the source be with you!
Authors: name: Raymond Li, name: Loubna Ben Allal, name: Yangtian Zi, name: Niklas Muennighoff, name: Denis Kocetkov, name: Chenghao Mou, name: Marc Marone, name: Christopher Akiki, name: Jia Li, name: Jenny Chim, name: Qian Liu, name: Evgenii Zheltonozhskii, name: Terry Yue Zhuo, name: Thomas Wang, name: Olivier Dehaene, name: Mishig Davaadorj, name: Joel Lamy-Poirier, name: João Monteiro, name: Oleh Shliazhko, name: Nicolas Gontier, name: Nicholas Meade, name: Armel Zebaze, name: Ming-Ho Yee, name: Logesh Kumar Umapathi, name: Jian Zhu, name: Benjamin Lipkin, name: Muhtasham Oblokulov, name: Zhiruo Wang, name: Rudra Murthy, name: Jason Stillerman, name: Siva Sankalp Patel, name: Dmitry Abulkhanov, name: Marco Zocca, name: Manan Dey, name: Zhihan Zhang, name: Nour Fahmy, name: Urvashi Bhattacharyya, name: Wenhao Yu, name: Swayam Singh, name: Sasha Luccioni, name: Paulo Villegas, name: Maxim Kunakov, name: Fedor Zhdanov, name: Manuel Romero, name: Tony Lee, name: Nadav Timor, name: Jennifer Ding, name: Claire Schlesinger, name: Hailey Schoelkopf, name: Jan Ebert, name: Tri Dao, name: Mayank Mishra, name: Alex Gu, name: Jennifer Robinson, name: Carolyn Jane Anderson, name: Brendan Dolan-Gavitt, name: Danish Contractor, name: Siva Reddy, name: Daniel Fried, name: Dzmitry Bahdanau, name: Yacine Jernite, name: Carlos Muñoz Ferrandis, name: Sean Hughes, name: Thomas Wolf, name: Arjun Guha, name: Leandro von Werra, name: Harm de Vries

The BigCode community, an open-scientific collaboration working on the
responsible development of Large Language Models for Code (Code LLMs),
introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context
length, infilling capabilities and fast large-batch inference enabled by
multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced
from The Stack, a large collection of permissively licensed GitHub repositories
with inspection tools and an opt-out process. We fine-tuned StarCoderBase on
35B Python tokens, resulting in the creation of StarCoder. We perform the most
comprehensive evaluation of Code LLMs to date and show that StarCoderBase
outperforms every open Code LLM that supports multiple programming languages
and matches or outperforms the OpenAI code-cushman-001 model. Furthermore,
StarCoder outperforms every model that is fine-tuned on Python, can be prompted
to achieve 40\% pass@1 on HumanEval, and still retains its performance on other
programming languages. We take several important steps towards a safe
open-access model release, including an improved PII redaction pipeline and a
novel attribution tracing tool, and make the StarCoder models publicly
available under a more commercially viable version of the Open Responsible AI
Model license.