RigoChat 2: an adapted language model to Spanish using a bounded dataset and reduced hardware
This work addresses the problem of resource-intensive LLM adaptation for Spanish-language users, though it appears incremental as it builds on existing methods.
The authors tackled the challenge of high computational costs in large language models by adapting a pretrained LLM to Spanish tasks with minimal resources, achieving superior results in Spanish-language tasks.
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.