ModernGBERT: German-only 1B Encoder Model Trained from Scratch
Authors: name: Anton Ehrmanntraut, name: Julia Wunderle, name: Jan Pfister, name: Fotis Jannidis, name: Andreas Hotho

Despite the prominence of decoder-only language models, encoders remain
crucial for resource-constrained applications. We introduce ModernGBERT (134M,
1B), a fully transparent family of German encoder models trained from scratch,
incorporating architectural innovations from ModernBERT. To evaluate the
practical trade-offs of training encoders from scratch, we also present
LL\"aMmlein2Vec (120M, 1B, 7B), a family of encoders derived from German
decoder-only models via LLM2Vec. We benchmark all models on natural language
understanding, text embedding, and long-context reasoning tasks, enabling a
controlled comparison between dedicated encoders and converted decoders. Our
results show that ModernGBERT 1B outperforms prior state-of-the-art German
encoders as well as encoders adapted via LLM2Vec, with regard to performance
and parameter-efficiency. All models, training data, checkpoints and code are
publicly available, advancing the German NLP ecosystem with transparent,
high-performance encoder models.