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Jan Ebert

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2

ECAI Conference 2025 Conference Paper

Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs

  • Mehdi Ali
  • Michael Fromm 0001
  • Klaudia Thellmann
  • Jan Ebert
  • Alexander Arno Weber
  • Richard Rutmann
  • Charvi Jain
  • Max Lübbering

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 Union. Trained on a dataset comprising around 60% non-English data and utilizing a custom multilingual tokenizer, our models address the limitations of existing Large Language Models (LLMs) that predominantly focus on English or a few high-resource languages. We detail the models’ development principles, i. e. , data composition, tokenizer optimization, and training methodologies. The models demonstrate strong performance across multilingual benchmarks, as evidenced by their performance on European versions of ARC, HellaSwag, and TruthfulQA.

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TMLR Journal 2023 Journal Article

StarCoder: may the source be with you!

  • Raymond Li
  • Loubna Ben allal
  • Yangtian Zi
  • Niklas Muennighoff
  • Denis Kocetkov
  • Chenghao Mou
  • Marc Marone
  • Christopher Akiki

The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model. Furthermore, StarCoder outperforms every model that is fine-tuned on Python and still retains its performance on other programming languages. We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license.

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