Tiny Aya: Bridging Scale and Multilingual Depth
This provides an efficient and balanced alternative for multilingual AI deployment, benefiting users in diverse regions by addressing scale and depth issues.
The paper tackled the challenge of creating a small multilingual language model that achieves state-of-the-art translation quality and strong multilingual understanding with only 3.35B parameters, trained on 70 languages and refined through region-aware posttraining.
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.