EuroLLM-22B: Technical Report

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Authors: Miguel Moura Ramos, Duarte Alves, Hippolyte Gisserot-Boukhlef, Alves, João, Pedro Henrique Martins, Patrick Fernandes, José Pombal, Nuno M. Guerreiro, Ricardo Rei, Nicolas Boizard, M. Amin Farajian, Mateusz Klimaszewski
+6 more José G. C. de Souza, Barry Haddow, François Yvon, Pierre Colombo, Alexandra Birch, André F. T. Martins
Cited by: 0

Abstract

This report presents EuroLLM-22B, a large language model trained from scratch to support the needs of European citizens by covering all 24 official European Union languages and 11 additional languages. EuroLLM addresses the issue of European languages being underrepresented and underserved in existing open large language models. We provide a comprehensive overview of EuroLLM-22B's development, including tokenizer design, architectural specifications, data filtering, and training procedures. Across a broad set of multilingual benchmarks, EuroLLM-22B demonstrates strong performance in reasoning, instruction following, and translation, achieving results competitive with models of comparable size. To support future research, we release our base and instruction-tuned models, our multilingual web pretraining data and updated EuroBlocks instruction datasets, as well as our pre-training and evaluation codebases.

Fields of Study & Subjects

Topic Modeling Natural Language Processing Techniques Multimodal Machine Learning Applications

Publication details

Journal / source
arXiv (Cornell University)
Publication date
5 February 2026
Document type
Preprint