Introduction

Language is an important part of communication, education, and access to information. However, people who speak languages with fewer digital resources may face difficulties when using artificial intelligence tools. Many language models work well with widely used languages, such as English, but their performance can be different when handling less represented languages.

Our project focuses on Kinyarwanda-to-English translation . The goal is to explore how artificial intelligence can help translate Kinyarwanda sentences into clear and meaningful English. While studying Natural Language Processing (NLP), I learned about Transformers, attention mechanisms, GPT-style models, and how large language models generate text. These concepts helped me understand how modern language models process and produce language.

In this blog, I compare three open-source language models: Qwen3-4B-Instruct-2507, Llama 3.2 3B Instruct, and SmolLM3-3B. I then select one model for further testing in my project.

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