Nicolas Chapados

Md. Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

Harm de Vries

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (see more)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (preprint)

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov

Raymond Li

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (see 46 more)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Manan Dey

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

Harm de Vries

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (see more)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (preprint)

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov

Raymond Li

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (see 46 more)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Manan Dey

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Md. Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

Harm de Vries

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (see more)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (preprint)

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov

Raymond Li

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (see 46 more)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Manan Dey

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Md. Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

Harm de Vries

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (see more)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (preprint)

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov

Raymond Li

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (see 46 more)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Manan Dey

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Md. Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

Harm de Vries

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (see more)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (preprint)

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov

Raymond Li

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (see 46 more)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Manan Dey

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Md. Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

Harm de Vries

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (see more)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (preprint)

TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series

Arjun Ashok

Étienne Marcotte

Valentina Zantedeschi

Alexandre Drouin

We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including fore… (see more)casting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS), wherein the number of distributional parameters now scales linearly with the number of variables instead of factorially. The new objective requires the introduction of a training curriculum, which goes hand-in-hand with necessary changes to the original architecture. We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks, while maintaining the flexibility of prior work, such as seamless handling of unaligned and unevenly-sampled time series. Code is made available at https://github.com/ServiceNow/TACTiS.

2024-01-16

ICLR.cc/2024/Conference (poster)

openreview.net

XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference

Jo˜ao Monteiro

Étienne Marcotte

Pierre-Andre Noel

Valentina Zantedeschi

David Vazquez

Chris Pal

Perouz Taslakian

In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference informati… (see more)on. Just-in-time processing of a context is inefficient due to the quadratic cost of self-attention operations, and caching is desirable. However, caching transformer states can easily require almost as much space as the model parameters. When the right context isn't known in advance, caching ICL can be challenging. This work addresses these limitations by introducing models that, inspired by the encoder-decoder architecture, use cross-attention to condition generation on reference text without the prompt. More precisely, we leverage pre-trained decoder-only models and only train a small number of added layers. We use Question-Answering (QA) as a testbed to evaluate the ability of our models to perform conditional generation and observe that they outperform ICL, are comparable to fine-tuned prompted LLMs, and drastically reduce the space footprint relative to standard KV caching by two orders of magnitude.

2024-01-01

EMNLP (Findings) (published)

Capture the Flag: Uncovering Data Insights with Large Language Models

Issam Hadj Laradji

Perouz Taslakian

Sai Rajeswar

Valentina Zantedeschi

Alexandre Lacoste

David Vazquez

Chris Pal

Alexandre Drouin

The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making. Howev… (see more)er, accomplishing this task requires considerable technical skills, domain expertise, and human labor. This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data, leveraging recent advances in reasoning and code generation techniques. We propose a new evaluation methodology based on a"capture the flag"principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset. We further propose two proof-of-concept agents, with different inner workings, and compare their ability to capture such flags in a real-world sales dataset. While the work reported here is preliminary, our results are sufficiently interesting to mandate future exploration by the community.

2023-12-21

ArXiv (preprint)

Capture the Flag: Uncovering Data Insights with Large Language Models

Issam Hadj Laradji

Perouz Taslakian

Sai Rajeswar

Valentina Zantedeschi

Alexandre Lacoste

David Vazquez

Chris Pal

Alexandre Drouin

The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making. Howev… (see more)er, accomplishing this task requires considerable technical skills, domain expertise, and human labor. This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data, leveraging recent advances in reasoning and code generation techniques. We propose a new evaluation methodology based on a"capture the flag"principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset. We further propose two proof-of-concept agents, with different inner workings, and compare their ability to capture such flags in a real-world sales dataset. While the work reported here is preliminary, our results are sufficiently interesting to mandate future exploration by the community.

2023-12-21

ArXiv (preprint)

Capture the Flag: Uncovering Data Insights with Large Language Models

Issam Hadj Laradji

Perouz Taslakian

Sai Rajeswar

Valentina Zantedeschi

Alexandre Lacoste