13 research outputs found
The Stack: 3 TB of permissively licensed source code
Large Language Models (LLMs) play an ever-increasing role in the field of
Artificial Intelligence (AI)--not only for natural language processing but also
for code understanding and generation. To stimulate open and responsible
research on LLMs for code, we introduce The Stack, a 3.1 TB dataset consisting
of permissively licensed source code in 30 programming languages. We describe
how we collect the full dataset, construct a permissively licensed subset,
present a data governance plan, discuss limitations, and show promising results
on text2code benchmarks by training 350M-parameter decoders on different Python
subsets. We find that (1) near-deduplicating the data significantly boosts
performance across all experiments, and (2) it is possible to match previously
reported HumanEval and MBPP performance using only permissively licensed data.
We make the dataset available at https://hf.co/BigCode, provide a tool called
"Am I in The Stack" (https://hf.co/spaces/bigcode/in-the-stack) for developers
to search The Stack for copies of their code, and provide a process for code to
be removed from the dataset by following the instructions at
https://www.bigcode-project.org/docs/about/the-stack/
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks
based on a few demonstrations or natural language instructions. While these
capabilities have led to widespread adoption, most LLMs are developed by
resource-rich organizations and are frequently kept from the public. As a step
towards democratizing this powerful technology, we present BLOOM, a
176B-parameter open-access language model designed and built thanks to a
collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer
language model that was trained on the ROOTS corpus, a dataset comprising
hundreds of sources in 46 natural and 13 programming languages (59 in total).
We find that BLOOM achieves competitive performance on a wide variety of
benchmarks, with stronger results after undergoing multitask prompted
finetuning. To facilitate future research and applications using LLMs, we
publicly release our models and code under the Responsible AI License
ChenghaoMou/text-dedup: Reference Snapshot
Various improvements were made to the Python scripts. Huge thanks to @chris-ha458!
✨ Spark improvements from the latest BigCode experiments
Genetic factors define CPO and CLO subtypes of nonsyndromicorofacial cleft.
Nonsyndromic orofacial cleft (NSOFC) is a severe birth defect that occurs early in embryonic development and includes the subtypes cleft palate only (CPO), cleft lip only (CLO) and cleft lip with cleft palate (CLP). Given a lack of specific genetic factor analysis for CPO and CLO, the present study aimed to dissect the landscape of genetic factors underlying the pathogenesis of these two subtypes using 6,986 cases and 10,165 controls. By combining a genome-wide association study (GWAS) for specific subtypes of CPO and CLO, as well as functional gene network and ontology pathway analysis, we identified 18 genes/loci that surpassed genome-wide significance (P < 5 Ă— 10-8) responsible for NSOFC, including nine for CPO, seven for CLO, two for both conditions and four that contribute to the CLP subtype. Among these 18 genes/loci, 14 are novel and identified in this study and 12 contain developmental transcription factors (TFs), suggesting that TFs are the key factors for the pathogenesis of NSOFC subtypes. Interestingly, we observed an opposite effect of the genetic variants in the IRF6 gene for CPO and CLO. Moreover, the gene expression dosage effect of IRF6 with two different alleles at the same single-nucleotide polymorphism (SNP) plays important roles in driving CPO or CLO. In addition, PAX9 is a key TF for CPO. Our findings define subtypes of NSOFC using genetic factors and their functional ontologies and provide a clue to improve their diagnosis and treatment in the future
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
International audienceAs language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a values-driven undertaking, putting issues of ethics, harm, and governance in the foreground. This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1.6TB dataset spanning 59 languages that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM) language model. We further release a large initial subset of the corpus and analyses thereof, and hope to empower large-scale monolingual and multilingual modeling projects with both the data and the processing tools, as well as stimulate research around this large multilingual corpus
StarCoder: may the source be with you!
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