Australia's Whole-of-Government Microsoft 365 Copilot Trial
Australia
Australia's DTA ran the world's largest whole-of-government Copilot trial — 5,000+ staff, almost 60 agencies — with an independent Nous …
Portugal · Lisbon · See the Portugal profile
A five-university consortium built AMÁLIA, Portugal's first open-source LLM for European Portuguese, for about €7m in PRR funds, launched 1 July 2026 at 9 billion parameters. It is in limited beta with four pilot users; independent press calls it "not yet fully operational".
Portugal sought sovereign AI infrastructure for European Portuguese amid a broader European debate on AI sovereignty.
AMÁLIA is Portugal's first large language model built specifically for European Portuguese, intended as open-source public digital-sovereignty infrastructure.
Developed by a five-university consortium — coordinated by NOVA University Lisbon with Instituto Superior Técnico and the universities of Coimbra, Porto and Minho, involving more than 60 researchers — the model extends the open, European-funded EuroLLM-9B foundation model to 9 billion parameters, plus separate vision and speech-recognition components and a 32,000-token context window. It was released open-source under an Apache 2.0 licence on Hugging Face, funded through Portugal's Recovery and Resilience Plan (PRR).
Publicly unveiled 1 July 2026, AMÁLIA has no public chat interface and experimental search capability only; it is running in a limited beta with four validated pilot users (museum, science, media and education bodies) for tasks such as answering visitor questions about artworks. The project cost around EUR 7 million through 2027, with a roadmap toward a 22-billion-parameter version by 2027.
Independent Portuguese and international press explicitly caution that the model 'is not yet fully operational,' that it is an adaptation of an existing multilingual model rather than an original build, and that at 9 billion parameters it is far smaller than leading commercial systems — an honest, evidence-thin starting point rather than a finished public-sector deployment.
Where this practice's information was retrieved from, and when.
Australia
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Singapore
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Netherlands
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Germany
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