4 citations · 6 across the 5 of their papers we have counts for
5 papers
Modalities, a PyTorch-native Framework For Large-scale LLM Training and Research
Max Lübbering, Timm Ruland, Richard Rutmann +8
Today's LLM (pre-) training and research workflows typically allocate a significant amount of compute to large-scale ablation studies. Despite the substantial compute costs of thes…
Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models
Mehdi Ali, Manuel Brack, Max Lübbering +15
High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets r…
Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs
Mehdi Ali, Michael Fromm, Klaudia Thellmann +38
We present two multilingual LLMs, Teuken 7B-base and Teuken 7B-instruct, designed to embrace Europe's linguistic diversity by supporting all 24 official languages of the European U…
Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions?
Alexander Arno Weber, Klaudia Thellmann, Jan Ebert +4
The adaption of multilingual pre-trained LLMs into eloquent and helpful assistants is essential to facilitate their use across different language regions. In that spirit, we are th…
Tokenizer Choice For LLM Training: Negligible or Crucial?
Mehdi Ali, Michael Fromm, Klaudia Thellmann +18
The recent success of Large Language Models (LLMs) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and…