From e2ae08dc447f8672bb559e4dfe05e77121eb46ba Mon Sep 17 00:00:00 2001 From: Miltos Allamanis Date: Sat, 23 Sep 2023 13:48:17 +0100 Subject: [PATCH] Add one more paper --- _publications/muennighoff2023octopack.markdown | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 _publications/muennighoff2023octopack.markdown diff --git a/_publications/muennighoff2023octopack.markdown b/_publications/muennighoff2023octopack.markdown new file mode 100644 index 00000000..3e5483d7 --- /dev/null +++ b/_publications/muennighoff2023octopack.markdown @@ -0,0 +1,12 @@ + +--- +layout: publication +title: "OctoPack: Instruction Tuning Code Large Language Models" +authors: Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, Shayne Longpre +conference: +year: 2023 +additional_links: +- {name: "ArXiV", url: "https://arxiv.org/abs/2308.07124"} +tags: ["dataset", "instruction tuning"] +--- +Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack.