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arXiv:2602.05879cs.CL

EuroLLM-22B: Technical Report

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

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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.

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Subjects
cs.CLComputation and Language cs.AIArtificial Intelligence cs.LGMachine Learning

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    • I did not know about EuroLLM. I had a look to the paper (https://arxiv.org/abs/2602.05879) describing it: Specifically, we discard documents shorter than 200 characters (Xue et al., 2021a), and any page containing the phrase “lorem ipsum,” the word “javascript,” or curly…

      Alexander-BarthView on HNParent context

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