Tiny Aya

local multilingual at 3.35B is interesting - have you benchmarked against the usual monolingual fine-tune approach? curious if regional specialization actually outperforms at task level.

It's a big deal for accessibility. The focus on underserved regions instead of just adding more European languages is the right call - there's a massive gap there. How does Tiny Aya perform on Hebrew specifically? And is it practical to fine-tune on domain-specific data at this size, or is 3.35B too small for meaningful customization?

Hi everyone!What stands out about Tiny Aya is that @Cohere did not treat multilingual AI as one flat problem.Instead of forcing 70+ languages into one generic model, they built a 3.35B family with regional specialization: Earth for Africa and West Asia, Fire for South Asia, and Water for Asia-Pacific and Europe. That is a much smarter way to get stronger linguistic grounding and cultural nuance while still keeping the model small enough for local deployment.Tiny Aya is built to run where people actually are: on local devices, in classrooms, in community labs, and in places where large-scale cloud infrastructure is not a given.That is a pretty meaningful direction for multilingual AI.