Domain-Level Explainability - A Challenge for Creating Trust in Superhuman AI Strategies. - Jonas Andrulis; Ole Meyer; Grégory Schott; Samuel Weinbach; Volker Gruhn (CoRR, 2020)

MAGMA - Multimodal Augmentation of Generative Models through Adapter-based Finetuning. - Constantin Eichenberg; Sidney Black; Samuel Weinbach; Letitia Parcalabescu; Anette Frank (EMNLP (Findings), 2022)

GPT-NeoX-20B: An Open-Source Autoregressive Language Model. - Sid Black; Stella Biderman; Eric Hallahan; Quentin Anthony; Leo Gao; Laurence Golding; Horace He; Connor Leahy; Kyle McDonell; Jason Phang; Michael Pieler; USVSN Sai Prashanth; et al. (CoRR, 2022)

M-VADER: A Model for Diffusion with Multimodal Context. - Samuel Weinbach; Marco Bellagente; Constantin Eichenberg; Andrew Dai 001; Robert Baldock; Souradeep Nanda; Björn Deiseroth; Koen Oostermeijer; Hannah Teufel; Andrés Felipe Cruz-Salinas (CoRR, 2022)

MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation. - Marco Bellagente; Manuel Brack; Hannah Teufel; Felix Friedrich; Björn Deiseroth; Constantin Eichenberg; Andrew Dai 001; Robert Baldock; Souradeep Nanda; Koen Oostermeijer; Andrés Felipe Cruz-Salinas; Patrick Schramowski; et al. (NeurIPS, 2023)

ATMAN: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation. - Björn Deiseroth; Mayukh Deb; Samuel Weinbach; Manuel Brack; Patrick Schramowski; Kristian Kersting (NeurIPS, 2023)

AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation. - Mayukh Deb; Björn Deiseroth; Samuel Weinbach; Patrick Schramowski; Kristian Kersting (CoRR, 2023)

Efficient Parallelization Layouts for Large-Scale Distributed Model Training. - Johannes Hagemann; Samuel Weinbach; Konstantin Dobler; Maximilian Schall; Gerard de Melo (CoRR, 2023)

T-FREE: Subword Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings. - Björn Deiseroth; Manuel Brack; Patrick Schramowski; Kristian Kersting; Samuel Weinbach (EMNLP, 2024)

Tokenizer Choice For LLM Training: Negligible or Crucial? - Mehdi Ali; Michael Fromm 001; Klaudia Thellmann; Richard Rutmann; Max Lübbering; Johannes Leveling; Katrin Klug; Jan Ebert; Niclas Doll; Jasper Schulze Buschhoff; Charvi Jain; Alexander Arno Weber; et al. (NAACL-HLT (Findings), 2024)

T-FREE: Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings. - Björn Deiseroth; Manuel Brack; Patrick Schramowski; Kristian Kersting; Samuel Weinbach (CoRR, 2024)

Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs. - Mehdi Ali; Michael Fromm 001; Klaudia Thellmann; Jan Ebert; Alexander Arno Weber; Richard Rutmann; Charvi Jain; Max Lübbering; Daniel Steinigen; Johannes Leveling; Katrin Klug; Jasper Schulze Buschhoff; et al. (CoRR, 2024)

u-μP: The Unit-Scaled Maximal Update Parametrization. - Charlie Blake; Constantin Eichenberg; Josef Dean; Lukas Balles; Luke Yuri Prince; Björn Deiseroth; Andrés Felipe Cruz-Salinas; Carlo Luschi; Samuel Weinbach; Douglas Orr (ICLR, 2025)