TranslatePsy-Nano: Compact Multilingual Machine Translation for Resource-Constrained Edge Deployment

Tether AI Research introduces TranslatePsy-Nano, compact multilingual machine translation model families designed for offline browser, mobile, desktop, and edge deployment, with EuroNano covering English and nine European languages and AfriNano covering English and eight African languages. Rather than using separate bilingual checkpoints, each family uses a single language-group-specific model directed by target-language tags and offered in Tiny, Base-Memory, and Base variants ranging from 17 MB to 42 MB when quantized. Built with Marian NMT and Bergamot’s CPU-oriented recurrent-attention architecture, the models are trained on filtered multilingual data using language identification and adequacy scoring to reduce noisy sentence pairs. On European-to-English evaluation, the Base model reports a 0.860 average COMET score, described as retaining 98.4% of NLLB-200’s quality while requiring far less storage, and the African Base model reports average COMET scores of 0.694 for African-to-English and 0.726 for English-to-African translation. Benchmark results also indicate substantially smaller deployment bundles, lower memory requirements, and faster CPU throughput than NLLB-200, while Android tests suggest practical offline performance, though translation into non-English target languages and direct translations between two supported non-English languages remain more demanding.

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