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title: Swahili Developer AI
emoji: 🌍
colorFrom: red
colorTo: blue
sdk: gradio
pinned: true
---
# 🌍 Swahili Developer AI
We’re a small but growing effort focused on building AI that actually makes sense in African contexts.
It started with Swahili, but it’s not just about one language. We care about **how AI works across African languages, systems, and real-world environments** — especially where data is limited and infrastructure isn’t perfect.
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## What we’re trying to do
Most AI today is built somewhere else, for somewhere else.
We’re interested in flipping that — building models and tools that:
- understand local languages (not just translate them)
- work in low-resource settings
- can actually be used in sectors like healthcare and education
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## What we work on
### Language models
We experiment with fine-tuning and adapting LLMs for:
- Swahili and other African languages
- Instruction-following and reasoning
- Domain-specific use (like healthcare)
### Multimodal AI
Not everything is text. We’re also exploring:
- Medical images (like X-rays)
- Document understanding
- Combining text + vision in practical ways
### Healthcare AI
A big area of interest for us:
- Clinical decision support
- Simple, reliable tools for frontline health workers
- Models that can run even with limited connectivity
### Data
A lot of the challenge is data.
We spend time on:
- Building and cleaning datasets
- Working with low-resource and noisy data
- Figuring out what “good evaluation” looks like outside English benchmarks
### Running models locally
We care about making things usable:
- Smaller, efficient models
- LoRA / quantization
- Running models on limited hardware
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## Projects (ongoing)
- **MedAI Africa** – exploring multimodal AI for clinical support
- **African Instruction Data** – building datasets for training and evaluation
- **Local LLM workflows** – simple pipelines for fine-tuning and deployment
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## How we think about this
We’re not trying to chase hype.
We’re more interested in:
- What works in practice
- What can be deployed
- What actually helps someone on the ground
AI doesn’t need to be bigger — it needs to be **more relevant**.
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## Collaboration
If you’re working on similar problems — especially around:
- African languages
- Healthcare AI
- low-resource ML
We're open to collaborating.
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## Tech we use
Mostly practical stuff:
- Transformers, PEFT, LoRA
- PyTorch / PyTorch Lightning
- FAISS for retrieval
- FastAPI, Gradio
- some ONNX / TensorRT when needed
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## Long-term
We’d like to see:
- Stronger datasets from Africa
- Models that understand context, not just language
- More local ownership of AI systems
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## Follow / Contribute
If this resonates, feel free to follow or contribute.
Let’s build things that actually work.
👉 https://huggingface.co/swahilidevelopers
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