At Msingi AI, we're revolutionizing natural language processing for African languages through innovative research approaches that combine technical excellence with cultural understanding.
Explore our ongoing research initiatives aimed at democratizing AI access across Africa.
Building authentic Kenyan Swahili Text-to-Speech that truly captures the natural flow and character of our language, setting a new standard for African language voice technology.
Identifying and mitigating biases in NLP models trained on African languages to ensure AI tools work equitably for all African users.
Developing specialized tokenizers for African languages that handle complex morphology, tonal systems, and linguistic variations with high efficiency.
Our flagship open-source Swahili language model built as a decoder-only transformer. Designed for efficiency and performance in resource-constrained environments while delivering high-quality natural language processing for Swahili text generation and understanding.
Exploring novel neural architectures with learnable activation functions to improve efficiency and performance in low-resource African language tasks.
Developing efficient techniques for pretraining language models with limited computational resources, making AI more accessible to researchers in low-resource settings.
Stay tuned for additional research initiatives
Our flagship open-source Swahili language model built to democratize AI access for Swahili speakers across East Africa.
Msingi1 is our state-of-the-art decoder-only transformer language model designed specifically for Swahili. With 12 layers, 768 hidden size, 12 attention heads, and approximately 110M parameters, it delivers impressive performance while remaining efficient enough to run on modest hardware.
The model features a 2048 token context length, Rotary Position Embeddings (RoPE), pre-norm transformer architecture with GELU activation, and optimizations like Flash Attention and gradient checkpointing for efficient training and inference.