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Tiny Aya: Bridging Scale and Multilingual Depth

Tiny Aya represents a significant advancement in compact multilingual artificial intelligence, offering a family of 3.35-billion-parameter models designed to decouple linguistic capability from massive scale. By prioritizing balanced performance across 70 languages, the researchers utilized a sophisticated data curation strategy that includes a unified multilingual tokenizer and region-aware posttraining to mitigate the disparities often found in low-resource language processing. The suite includes a pretrained foundation model, an instruction-tuned global variant, and specialized regional models—Earth, Fire, and Water—that leverage synthetic data generation and model merging to enhance translation quality and cultural nuance without sacrificing general instruction-following abilities. Rigorous evaluation demonstrates that Tiny Aya outperforms comparable open-weight models like Gemma3-4B in translation and safety metrics, particularly for underrepresented languages, effectively democratizing access to high-quality, efficient AI that can be deployed on consumer-grade edge devices. https://github.com/Cohere-Labs/tiny-a... https://huggingface.co/collections/Co...