ModernGBERT: German-only 1B Encoder Model Trained from Scratch
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.
683597e271c57c48d73d78a0
Anton Ehrmanntraut
683597e271c57c48d73d78a1
Julia Wunderle
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2025-05-27T12:30:53.989Z
61a36cba39c7c71f0bda980e
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Julia Wunderle 
user
Julia287
683597e271c57c48d73d78a2
Jan Pfister
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2025-05-27T12:13:08.065Z
6070431e1a4c4d313032558b
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/bPLvEgvzsZJXWamPaU6ou.png
Jan Pfister
user
JanPf
683597e271c57c48d73d78a3
Fotis Jannidis
683597e271c57c48d73d78a4
Andreas Hotho
ModernGBERT: German-only 1B Encoder Model Trained from Scratch
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.
ModernGBERT and LL\"aMmlein2Vec, new German encoder models, outperform existing models in terms of performance and parameter-efficiency across various NLP tasks.
encoder models ModernBERT LLM2Vec natural language understanding text embedding long-context reasoning parameter-efficiency German NLP