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EuroLLM-22B: Technical Report

About

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.

Miguel Moura Ramos, Duarte M. Alves, Hippolyte Gisserot-Boukhlef, Jo\~ao Alves, Pedro Henrique Martins, Patrick Fernandes, Jos\'e Pombal, Nuno M. Guerreiro, Ricardo Rei, Nicolas Boizard, Amin Farajian, Mateusz Klimaszewski, Jos\'e G. C. de Souza, Barry Haddow, Fran\c{c}ois Yvon, Pierre Colombo, Alexandra Birch, Andr\'e F. T. Martins• 2026

Related benchmarks

TaskDatasetResultRank
Code GenerationHumanEval--
1043
Instruction FollowingIFEval
IFEval Accuracy67.2
836
ReasoningBBH--
726
Science Question AnsweringARC Challenge
Accuracy89.8
354
General KnowledgeMMLU
MMLU General Knowledge Accuracy69.8
307
Mathematical ReasoningMGSM
Accuracy76.1
236
Common Sense ReasoningHellaSwag
Accuracy69.7
213
Mathematical ReasoningGSM8K
Math Score85.5
197
Scientific Question AnsweringGPQA Diamond
Accuracy26.8
123
MathematicsMATH 500
Pass@154.5
122
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