Developing a fully open-source family of Large Language Models for European languages
Pre-Training
Pre-Training: We develop new ways to explore and improve how large language models are trained. One of our key interests is finding simple rules, called scaling laws, that help predict how results from smaller models will carry over to larger ones. To do this, we draw on ideas from Automated Machine Learning (AutoML), which aims to make the process of building and improving machine learning systems more automatic and efficient.
Post-Training
Post-Training: We lead the evaluation workpackage, a core technical component of the project. Our team develops efficient evaluation infrastructure across computing clusters and creates transparent methods for assessing multilingual models—both pre-trained and instruction-tuned. We also work on improving instruction-tuning pipelines, advancing model capabilities and performance.