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README.md

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-title: README
-emoji: 📚
+title: Swahili Developer AI
+emoji: 🌍
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-Edit this `README.md` markdown file to author your organization card.
+# 🌍 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.
+
+---
+
+## 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
+
+---
+
+## 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  
+
+---
+
+## 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  
+
+---
+
+## 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**.
+
+---
+
+## Collaboration
+
+If you’re working on similar problems — especially around:
+- African languages  
+- healthcare AI  
+- low-resource ML  
+
+we’re open to collaborating.
+
+---
+
+## Tech we use
+
+Mostly practical stuff:
+- Transformers, PEFT, LoRA  
+- PyTorch / PyTorch Lightning  
+- FAISS for retrieval  
+- FastAPI, Gradio  
+- some ONNX / TensorRT when needed  
+
+---
+
+## Long-term
+
+We’d like to see:
+- stronger datasets from Africa  
+- models that understand context, not just language  
+- more local ownership of AI systems  
+
+---
+
+## Follow / Contribute
+
+If this resonates, feel free to follow or contribute.
+
+Let’s build things that actually work.
+
+👉 https://huggingface.co/swahilidevelopers
+
+---