% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Strube:1038632,
      author       = {Strube, Alexandre},
      title        = {{H}elmholtz {B}lablador - {A}n experimental {L}arge
                      {L}anguage {M}odel server},
      reportid     = {FZJ-2025-01603},
      year         = {2024},
      note         = {Talk in an internal format of Forschungszentrum Jülich.},
      abstract     = {Alexandre Strube (JSC) stellt in seinem Vortrag das Large
                      Language System Blablador vor, das im Rahmen der
                      HIFIS-Kooperation angeboten wird. Der Vortrag wird in
                      englischer Sprache gehalten.AbstractRecent advances in large
                      language models (LLMs) like chatGPT have demonstrated their
                      potential for generating human-like text and reasoning about
                      topics with natural language. However, applying these
                      advanced LLMs requires significant compute resources and
                      expertise that are out of reach for most academic
                      researchers. To make scientific LLMs more accessible, we
                      have developed Helmholtz Blablador, an open-source inference
                      server optimized for serving predictions from customized
                      scientific LLMs.Blablador provides the serving
                      infrastructure to make models accessible via a simple API
                      without managing servers, firewalls, authentication or
                      infrastructure. Researchers can add their pretrained LLMs to
                      the central hub. Other scientists can then query the
                      collective model catalog via web or using the popular OpenAI
                      api to add LLM functionality in other tools, like
                      programming IDEs.This enables a collaborative ecosystem for
                      scientific LLMs:Researchers train models using datasets and
                      GPUs from their own lab. No need to set up production
                      servers. They can even provide their models with inference
                      happening on cpus, with the use of tools like
                      llama.cpp.Models are contributed to the Blablador hub
                      through a web UI or API call. Blablador handles loading
                      models and publishing models for general use.Added models
                      become available for querying by other researchers.A model
                      catalog displays available LLMs from different labs and
                      research areas.Besides that, one can train, quantize,
                      fine-tune and evaluate LLMs directly with Blablador.The
                      inference server is available at
                      http://helmholtz-blablador.fz-juelich.de},
      organization  = {2. IT-Forum 2024, Jülich (Germany)},
      subtyp        = {Other},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / Helmholtz AI Consultant
                      Team FB Information (E54.303.11)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-Juel-1)E54.303.11},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/1038632},
}