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| # Smol-AI-Africa: The Kano Edition (v1.0) 🌍🇳🇬 |
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| **Lead Developer:** Ahmad Garba Adamu (AGABOT-99) |
| **System Architecture:** SmolLM2-135M (Fine-tuned via PEFT/LoRA) |
| **Operational Target:** 2GB RAM Mobile SoC (Low-Power ARMv8) |
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| ## 🏗️ 1. Technical Abstract |
| Smol-AI-Africa represents a breakthrough in **Low-Resource Natural Language Processing (LR-NLP)**. While modern LLMs are scaled toward trillion-parameter architectures, this project focuses on **Extreme Optimization** for the African digital frontier. |
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| ## 🔬 2. Engineering Methodology: 'Delicate Anchoring' |
| ### 2.1 Low-Rank Adaptation (LoRA) Parameters |
| We avoid full-parameter updates to prevent **Catastrophic Forgetting**. We apply a low-rank decomposition to the weight updates: |
| $$W = W_0 + \Delta W = W_0 + BA$$ |
| Using a **Rank (r) of 16** and **Alpha of 32**, we target the `q_proj` and `v_proj` modules for maximum efficiency on 2GB RAM devices. |
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| ## 🌍 3. Socio-Economic Impact |
| ### 3.1 Mobile Accessibility |
| In Kano—Africa's commercial nerve center—hardware is the gatekeeper. By optimizing for 2GB RAM, we ensure this AI runs on second-hand smartphones and older Android devices used by the 'Street Smart' hustle. |
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| ## 📊 4. Performance Specifications |
| | Metric | Specification | |
| | :--- | :--- | |
| | **Parameter Count** | 135 Million | |
| | **RAM Requirement** | < 2GB | |
| | **Inference Speed** | ~20 tokens/sec | |
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| ## 🚀 5. Implementation & Usage |
| To engage the persona, use the anchor code: |
| ```text |
| KANO-CORE-77 [Your Question] |
| ``` |
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| ### 🎓 About the Architect |
| **Ahmad Garba Adamu (AGABOT-99)** is an AI Researcher from **Kano, Nigeria**, building 'Glocal' solutions for the people. |
