EuroLLM-22B: Technical Report
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| author | Ramos, Miguel Moura Alves, Duarte M. Gisserot-Boukhlef, Hippolyte Alves, João Martins, Pedro Henrique Fernandes, Patrick Pombal, José Guerreiro, Nuno M. Rei, Ricardo Boizard, Nicolas Farajian, Amin Klimaszewski, Mateusz de Souza, José G. C. Haddow, Barry Yvon, François Colombo, Pierre Birch, Alexandra Martins, André F. T. |
| author_facet | Ramos, Miguel Moura Alves, Duarte M. Gisserot-Boukhlef, Hippolyte Alves, João Martins, Pedro Henrique Fernandes, Patrick Pombal, José Guerreiro, Nuno M. Rei, Ricardo Boizard, Nicolas Farajian, Amin Klimaszewski, Mateusz de Souza, José G. C. Haddow, Barry Yvon, François Colombo, Pierre Birch, Alexandra Martins, André F. T. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_05879 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EuroLLM-22B: Technical Report Ramos, Miguel Moura Alves, Duarte M. Gisserot-Boukhlef, Hippolyte Alves, João Martins, Pedro Henrique Fernandes, Patrick Pombal, José Guerreiro, Nuno M. Rei, Ricardo Boizard, Nicolas Farajian, Amin Klimaszewski, Mateusz de Souza, José G. C. Haddow, Barry Yvon, François Colombo, Pierre Birch, Alexandra Martins, André F. T. Computation and Language Artificial Intelligence Machine Learning 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. |
| title | EuroLLM-22B: Technical Report |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2602.05879 |