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nacef112/LelapaAI-InkubaLM-Compression

Domain:

natural language processing

Record type:

model
Creator:
nac
Host:
Submission for Zindi's Lelapa AI Buzuzu-Mavi Challenge - Bronze Medalist. Focused model compression for InkubaLM targeting Swahili and Hausa. # 🧠 InkubaLM Compression for Swahili & Hausa ### 🥉 Bronze Medal Solution – Zindi Lelapa AI Buzuzu-Mavi Challenge --- ## 🚀 Overview Open-source language models often underperform on African languages and demand high computational resources—barriers to real-world use in the African context. To make language AI truly inclusive, we need models that are **smaller**, **smarter**, and optimized for **resource-constrained environments**. The **Lelapa AI Buzuzu-Mavi Challenge** tasked participants with compressing Lelapa AI’s *InkubaLM*—an open-source small language model (SLM)—while **maintaining or improving performance** for two key African languages: **Swahili** and **Hausa**. This repository presents our **Bronze Medal-winning solution**. 🥉 --- ## 🎯 Objectives ✅ Compress InkubaLM to reduce size and inference cost ✅ Retain or improve model accuracy on core NLP tasks ✅ Ensure usability on low-resource devices and CPUs ✅ Focus on **Swahili** and **Hausa** performance --- ## 🧪 Tasks & Evaluation The model was evaluated across three NLP tasks: - 🗣️ **Sentiment Analysis** - 🧠 **Natural Language Inference** (AfriXNLI – true/false reasoning) - 🌍 **Machine Translation** (English → Swahili & Hausa) Performance could be improved by either: - Increasing task accuracy, - Reducing model size, - Or both. --- ## 🛠️ Techniques Applied 🔧 **Quantization** – Reduced precision (8-bit & 4-bit) for faster, leaner models ✂️ **Pruning** – Removed redundant parameters 🌐 **Language-Specific Fine-tuning** – Custom fine-tuning on Swahili and Hausa datasets --- ## 🌍 Why It Matters This work moves us closer to a future where African languages have **equal representation** in the AI ecosystem. Smaller, smarter models enable: - ✅ Faster NLP on standard CPUs - ✅ Offline language tools - ✅ Scalable deployment in education, agriculture, health, and customer service --- ## 🏗️ Repository Structure