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University of Turku and SiloGen launch consortium to build the world’s largest open LLM

Oct 19, 2023 - utu.fi
SiloGen and the TurkuNLP research group at the University of Turku have launched a consortium to develop the world's largest open-source language model, covering all official European languages. The initiative, which has access to approximately 15 million GPU hours, aims to ensure that the data used in these models accurately represents European languages, including the English-speaking world. The consortium, which includes leading scholars and AI experts, will use resources such as High-Performance Language Technology data and the LUMI supercomputer for language model training.

The consortium's goal is to democratise access to large language models (LLMs) and enable the development of use-case specific downstream applications. The initiative is also committed to adhering to European regulations and working closely with key European institutions. The TurkuNLP research group and Silo AI, with their extensive experience in natural language processing and generative AI, aim to redefine the boundaries of open-source language models. The consortium also plans to provide companies with a unique path to create value using independent, trusted, and secure base models.

Key takeaways:

  • SiloGen and TurkuNLP research group at the University of Turku have launched a consortium to develop the world’s largest open source language model, covering all official European languages.
  • The initiative is dedicated to ensuring that data used in these models accurately represent European languages, and is conducted in collaboration with key European institutions and agencies.
  • The consortium combines world-class LLM team, data resources covering all European languages and code, and access to compute, including the LUMI supercomputer for LLM training.
  • The TurkuNLP research group’s experience in NLP and LLMs aligns with Silo AI’s and SiloGen’s commitment to contribute to world-class research on generative AI, providing a robust foundation for redefining the boundaries of open source language models.
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