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，这是一个拥有13亿参数的语言模型，在包含3万亿英文和法文token的数据集上预训练而成，旨在为研究和工业界提供一个高性能、完全开源的双语模型，并能在消费级本地硬件上快速运行。为此，我们开创性地开发了一种内在双语的模型训练方法，采用1:1的英语-法语预训练数据比例、自定义分词器以及双语微调数据集。我们发布了训练数据集，其中包含法语部分，该部分由人工精心筛选的高质量、多样化的数据源组成。为了评估模型在英语以外的语言上的表现，我们设计了一个新的基准测试FrenchBench，包含一系列分类和生成任务，覆盖法语模型性能的多个正交维度。此外，秉持透明性原则，并为进一步推动大型语言模型研究，我们发布了代码库、数十个不同规模、训练数据分布和训练步骤的检查点，以及经过微调的聊天模型和强大的翻译模型。我们通过FMTI框架评估模型，验证了81%的透明度标准，远超大多数甚至开源项目的得分。这项工作丰富了自然语言处理领域的研究，打破了以往以英语为中心的工作模式，以加深我们对语言模型中多语言性的理解。