This article explores a structured pruning technique for state-of-the-art models, that uses a GLU architecture, enabling the creation of smaller and more efficient large language models.
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Disclaimer: This article was originally written in Spanish and translated into English using AI tools as support to ensure accuracy and consistency. You can find the original Spanish version here.
As large language models continue to grow in size to achieve greater capabilities, the demand for more efficient, smaller versions has become more necessary than ever. However, reducing a model’s size without losing its core functionality is a delicate balancing act.
Techniques such as quantization and pruning are commonly used to decrease size, while methods like knowledge distillation or transfer learning help retain or recover the capabilities lost during the reduction process.
Among these, pruning stands out as one of the most effective strategies for reducing model size. Unlike quantization, which simplifies numerical representations, pruning involves removing specific parts of the model, such as neurons or entire layers. But this effectiveness comes at a cost: pruning…