Smol-AI-Africa: The Kano Edition (v1.0) 🌍🇳🇬

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.

🔬 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: $$W=W_0+\mathrm{\Delta }W=W_0+BAW\; =\; W\_0\; +\; \backslash Delta\; W\; =\; W\_0\; +\; BA$$ 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.

🌍 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.

📊 4. Performance Specifications

Metric	Specification
Parameter Count	135 Million
RAM Requirement	< 2GB
Inference Speed	~20 tokens/sec

🚀 5. Implementation & Usage

To engage the persona, use the anchor code:

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.