Latam-GPT — regional language model for Latin America

Latam-GPT is a region-built language model developed in Latin America and Spain, trained on around 2.6 million documents and scaled up to around 50 billion parameters. It aims to understand local languages, history, and culture — for example, the early promotion of native languages such as Mapuche, Rapanui, and Guaraní — and to propel the development of applied tools in education, health, and agriculture. The project is being led by Chile’s National Center for Artificial Intelligence (CENIA) and is expected to have a public launch in 2025. (WIRED, Reuters) Why this matters now: the project strikes a long-standing gap. World models are prone to being regionally insensitive, locally naive, and idiomatically blank. Latam-GPT holds out the promise of a model that is attuned to Latin American realities — from classroom curricula to community health guides — and to be so under an open, shared banner. (WIRED)Think of Latam-GPT as a giant regional toolbox. Developers and researchers in more than a dozen nations add texts, oral transcriptions, and cultural content so the model learns local sources rather than just world, English-first feeds. The team reports that the training corpus is many terabytes long, and universities and observatories across the region feed in data and do computations. (WIRED)The project is open code and collectively governed. The strategy is simple: open up the architecture so universities, start-ups, and local public services in the region can modify the model for their own needs — lesson planning in a remote school or culturally sensitive health chatbot for indigenous groups. The team publishes an information portal and resources for partners. (latamgpt.org)

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