Large Language Models (LLMs) have become a key element of modern artificial intelligence, demonstrating the ability to address a wide range of language processing tasks at unprecedented levels of accuracy without the need of collecting problem-specific data. However, these versatile models face a significant challenge: both their training and inference processes require substantial computational resources, time, and memory. Consequently, optimizing this kind of models to minimize these requirements is crucial. In this article, we demonstrate that, with minimal resources and in a remarkably short time, it is possible to enhance a state-of-the-art model, specifically for a given language task, without compromising its overall capabilities using a relatively small pretrained LLM as a basis. Specifically, we present our use case, RigoChat 2, illustrating how LLMs can be adapted to achieve superior results in Spanish-language tasks.

本研究针对大型语言模型（LLMs）在训练和推理过程中的高计算资源和时间需求，提出了一种优化方法。通过最小资源和时间，我们展示如何在相对较小的预训练LLM基础上增强模型，以适应西班牙语任务，取得了显著改进的结果。

RigoChat 2：使用受限数据集和降低的硬件适配西班牙语的语言模型