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.
1. Overview of Three Open-Source Language Models
Qwen3-4B-Instruct-2507
Qwen3-4B-Instruct-2507 was developed by the Qwen team at Alibaba Cloud. It has approximately four billion parameters and supports a context window of 262,144 tokens. Its licence is Apache 2.0, which allows users to use, modify, and distribute the model under the licence conditions.
This model is designed for instruction following, reasoning, text comprehension, mathematics, and code generation. Its developers also report improvements in multilingual capabilities. These features make it a useful candidate for testing language translation.
For our project, we can give the model a Kinyarwanda sentence and instruct it to return only the English translation. However, because it is a general-purpose language model, we must evaluate its Kinyarwanda translation quality rather than assume it will always produce accurate results.
Model card: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507
Llama 3.2 3B Instruct
Llama 3.2 3B Instruct was developed by Meta. It contains approximately 3.21 billion parameters and supports a context window of up to 128,000 tokens. It uses the Llama 3.2 Community License, which has specific conditions for use and distribution.
The model is intended for conversational assistance, summarisation, text generation, and information retrieval. Its relatively small size makes it worth considering for applications with limited computing resources.
However, Meta lists eight officially supported languages for Llama 3.2, and Kinyarwanda is not among them. This does not prove that it cannot translate Kinyarwanda, but its performance would need to be tested carefully.
Model card: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
SmolLM3-3B
SmolLM3-3B was developed by Hugging Face. It has approximately three billion parameters and supports a context window of up to 128,000 tokens. It is released under the Apache 2.0 licence.
SmolLM3-3B is designed for instruction following, reasoning, and general text generation. Hugging Face also provides information about its training data and methods, making it useful for learning and experimentation.
Its reported language support includes English, French, Spanish, German, Italian, and Portuguese. Kinyarwanda is not included in this list. Therefore, I would need to evaluate its translation performance before considering it suitable for our project.
Model card: https://huggingface.co/HuggingFaceTB/SmolLM3-3B
2. Comparison of the Models
The following table summarises the main differences between the three models.
| Feature | Qwen3-4B-Instruct-2507 | Llama 3.2 3B Instruct | SmolLM3-3B |
|---|---|---|---|
| Developer | Qwen team, Alibaba Cloud | Meta | Hugging Face |
| Parameters | 4 billion | 3.21 billion | 3 billion |
| Context window | 262,144 tokens | 128,000 tokens | Up to 128,000 tokens |
| Licence | Apache 2.0 | Llama 3.2 Community License | Apache 2.0 |
| Main strengths | Instruction following, reasoning, multilingual text tasks | Chat, summarisation, text generation | Reasoning and instruction following |
| Kinyarwanda support | Requires evaluation | Not officially listed | Not among reported supported languages |
The comparison shows that all three models can perform general language tasks, but they differ in size, licensing conditions, and reported language support. Qwen3-4B-Instruct-2507 has the largest parameter count and context window among these three models. However, model size and context length alone do not determine translation quality.
3. The Model I Choose and Why
After comparing the models, I choose Qwen3-4B-Instruct-2507 for further testing in my Kinyarwanda-to-English translation project.
First, it is suitable for experimentation with limited computing resources. A model with four billion parameters requires less memory than many much larger language models. In 16-bit precision, its weights require approximately eight gigabytes of memory. A free Google Colab T4 GPU has 16 GB of GPU memory, so running this model is a reasonable option if memory is managed carefully. Additional memory is required for processing inputs and generating translations. If necessary, I can use quantization to reduce memory requirements.
Second, the model weights are available under the Apache 2.0 licence. I can download and run the model using Python libraries such as Hugging Face Transformers without paying for a model API or supplying an API key. I must still follow the licence and any applicable platform terms. Free Colab access also has usage limits and does not guarantee that a T4 GPU will always be available.
Third, Qwen3-4B-Instruct-2507 is designed to follow instructions. This is important because my project needs a consistent output format. For example, I can instruct the model to translate a Kinyarwanda sentence into natural English and return only the translated sentence.
For a simple example, the input could be “Ndashaka kujya ku isoko.” The expected English translation is “I want to go to the market.” This example illustrates the task format, but it does not prove that the model will translate every sentence correctly.
Finally, I will evaluate the model using a collection of Kinyarwanda sentences paired with correct English translations. I will compare its outputs with the reference translations and use evaluation measures such as BLEU or chrF. I will also manually review examples to check meaning, grammar, and whether important information has been preserved.
Qwen3 is not specifically developed only for Kinyarwanda translation, so it may make mistakes with idioms, context, or uncommon words. If its performance is not satisfactory, I can explore improving the prompts or fine-tuning the model with suitable Kinyarwanda-English parallel data.
Conclusion
Open-source language models provide opportunities to explore NLP projects without depending entirely on paid APIs. Qwen3-4B-Instruct-2507, Llama 3.2 3B Instruct, and SmolLM3-3B each offer useful capabilities, but their reported language support and licensing conditions differ.
I selected Qwen3-4B-Instruct-2507 because of its instruction-following capabilities, multilingual features, Apache 2.0 licence, and potential to run on a free Google Colab T4 GPU. My next step is to test it with real Kinyarwanda-English examples and measure the quality of its translations. This evaluation will help determine whether the model is useful for my project and what improvements may be needed.
References
Qwen Team. Qwen3-4B-Instruct-2507 Model Card. Hugging Face. https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507
Meta. Llama 3.2 3B Instruct Model Card. Hugging Face. https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
Hugging Face. SmolLM3-3B Model Card. Hugging Face. https://huggingface.co/HuggingFaceTB/SmolLM3-3B
Vaswani, A., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems. https://arxiv.org/abs/1706.03762
Hugging Face. Transformers Documentation. https://huggingface.co/docs/transformers/
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