TranslatePsy-AfriSLM: Optimized Machine Translation for African Languages

TranslatePsy-AfriSLM is an open-source machine translation suite for 19 Sub-Saharan African languages designed to make multilingual AI more accessible on personal devices with limited connectivity. Developed by Tether AI Research, it fine-tunes 0.8B, 2B, and 4B parameter Qwen models using carefully filtered synthetic parallel data, with a quality-estimation pipeline combining AfriCOMET, SSA-COMET, and MetricX-24 to prioritize useful training examples over raw data volume. The authors report that filtering reduced an open-source training pool from 44.93 billion to 1.76 billion tokens without comparable performance loss, while their final 32.37-billion-token synthetic mixture enabled even the 0.8B model to match or exceed much larger translation and general-purpose models across several African translation benchmarks. The models also showed transfer to eight unseen African languages, strong zero-shot African-to-African translation despite English-centric training data, and retained conversational, language-identification, and instruction-following abilities. An additional Asia-Europe data mixture was used to reduce loss of performance in non-African languages, and the findings were checked with lexical metrics and LLM-based judging, though the authors note the need for systematic native-speaker evaluation and caution that automated data generation and quality filtering can propagate errors.

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