We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81 % of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.

翻译：我们提出CroissantLLM，这是一个基于3T英语和法语token预训练的13亿参数语言模型，旨在为研究和工业界提供一款高性能、完全开源的双语模型，可在消费级本地硬件上快速运行。为此，我们开创性地采用1:1英法预训练数据比例、定制分词器及双语微调数据集，训练出本质上的双语模型。我们公开了训练数据集，其中包含经过人工精心筛选、高质量且来源多样的法语数据子集。为评估非英语环境下的模型表现，我们构建了新型基准测试FrenchBench，涵盖分类与生成任务，覆盖法语语言模型性能的多项正交维度。此外，秉持透明原则并推动大语言模型研究发展，我们开源了代码库、涵盖不同模型规模、训练数据分布及训练步骤的数十个检查点、微调对话模型及强大翻译模型。通过FMTI框架评估，我们的模型满足了81%的透明度标准，远超多数开源项目。本研究丰富自然语言处理领域生态，突破以往英语中心化研究的局限，深化对语言模型多语能力的理解。