Tiny Aya：弥合规模与多语言深度
Tiny Aya: Bridging Scale and Multilingual Depth
AI总结：
Tiny Aya通过区域感知后训练，在仅3.35B参数下实现多语言翻译、理解和生成的高性能，提供高效且平衡的多语言AI解决方案。
摘要：
Tiny Aya重新定义了小型多语言语言模型所能实现的成就。该模型在70种语言上进行训练，并通过区域感知的后训练进行优化，实现了翻译质量、强大的多语言理解能力和高质量的目标语言生成，仅使用3.35B参数。发布内容包括一个预训练基础模型、一个全球平衡的指令微调变体，以及三个针对非洲、南亚、欧洲、亚太和西亚语言的区域专用模型。本报告详细介绍了Tiny Aya的训练策略、数据组成和综合评估框架，并提出了一种多语言AI的替代扩展路径：以效率为中心，实现语言间的平衡性能和实际部署。
英文摘要：
Tiny Aya redefines what a small multilingual language model can achieve. Trained on 70 languages and refined through region-aware posttraining, it delivers state-of-the-art in translation quality, strong multilingual understanding, and high-quality target-language generation, all with just 3.35B parameters. The release includes a pretrained foundation model, a globally balanced instruction-tuned variant, and three region-specialized models targeting languages from Africa, South Asia, Europe, Asia-Pacific, and West Asia. This report details the training strategy, data composition, and comprehensive evaluation framework behind Tiny Aya, and presents an alternative scaling path for multilingual AI: one centered on efficiency, balanced performance across languages, and practical deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
