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Can small language models revitalize Indigenous languages?
Artificial intelligence (AI) small language models (SLMs) are designed to perform specific language tasks using far fewer resources than larger counterparts that have dominated the field so far. As the release of DeepSeek-R1 displayed, techniques often used in SLMs such as distillation and reinforcement learning can result in models that can be trained faster, consume less energy, and run more efficiently on devices with limited computational power and low-bandwidth connectivity. Typically encompassing a range from a few million to a few billion parameters, SLMs are optimized for targeted tasks and fine-tuned on smaller datasets.
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