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In recent years, multilingual machine translation models have achievedpromising performance on low-resource language pairs by sharing informationbetween similar languages, thus enabling zero-shot translation. To overcome the"curse of multilinguality", these models often opt for scaling up the number ofparameters, which makes their use in resource-constrained environmentschallenging. We introduce SMaLL-100, a distilled version of the M2M-100 (12B)model, a massively multilingual machine translation model covering 100languages. We train SMaLL-100 with uniform sampling across all language pairsand therefore focus on preserving the performance of low-resource languages. Weevaluate SMaLL-100 on different low-resource benchmarks: FLORES-101, Tatoeba,and TICO-19 and demonstrate that it outperforms previous massively multilingualmodels of comparable sizes (200-600M) while improving inference latency andmemory usage. Additionally, our model achieves comparable results to M2M-100(1.2B), while being 3.6x smaller and 4.3x faster at inference. Code andpre-trained models: https://github.com/alirezamshi/small100

SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages