Samuel Weinbach

According to our database, Samuel Weinbach authored at least 13 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

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

Mehdi Ali, Michael Fromm, Klaudia Thellmann, Jan Ebert, Alexander Arno Weber, Richard Rutmann, Charvi Jain, Max Lübbering, Daniel Steinigen, Johannes Leveling, Katrin Klug, Jasper Schulze Buschhoff, Lena Jurkschat, Hammam Abdelwahab, Benny Jörg Stein, Karl-Heinz Sylla, Pavel Denisov, Nicolo' Brandizzi, Qasid Saleem, Anirban Bhowmick, Lennard Helmer, Chelsea Maria John, Pedro Ortiz Suarez, Malte Ostendorff, Alex Jude, Lalith Manjunath, Samuel Weinbach, Carolin Penke, Oleg Filatov, Shima Asaadi, Fabio Barth, Rafet Sifa, Fabian Küch, Andreas Herten, René Jäkel, Georg Rehm, Stefan Kesselheim, Joachim Köhler, Nicolas Flores-Herr

CoRR, 2024

u-μP: The Unit-Scaled Maximal Update Parametrization.

Charlie Blake, Constantin Eichenberg, Josef Dean, Lukas Balles, Luke Y. Prince, Björn Deiseroth, Andrés Felipe Cruz-Salinas, Carlo Luschi, Samuel Weinbach, Douglas Orr

CoRR, 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

Tokenizer Choice For LLM Training: Negligible or Crucial?

Mehdi Ali, Michael Fromm, Klaudia Thellmann, Richard Rutmann, Max Lübbering, Johannes Leveling, Katrin Klug, Jan Ebert, Niclas Doll, Jasper Schulze Buschhoff, Charvi Jain, Alexander Arno Weber, Lena Jurkschat, Hammam Abdelwahab, Chelsea John, Pedro Ortiz Suarez, Malte Ostendorff, Samuel Weinbach, Rafet Sifa, Stefan Kesselheim, Nicolas Flores-Herr

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2024, 2024

T-FREE: Subword Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings.

Björn Deiseroth, Manuel Brack, Patrick Schramowski, Kristian Kersting, Samuel Weinbach

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023

Efficient Parallelization Layouts for Large-Scale Distributed Model Training.

Johannes Hagemann, Samuel Weinbach, Konstantin Dobler, Maximilian Schall, Gerard de Melo

CoRR, 2023

AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation.

Mayukh Deb, Björn Deiseroth, Samuel Weinbach, Patrick Schramowski, Kristian Kersting

CoRR, 2023

ATMAN: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation.

Björn Deiseroth, Mayukh Deb, Samuel Weinbach, Manuel Brack, Patrick Schramowski, Kristian Kersting

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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, Robert Baldock, Souradeep Nanda, Koen Oostermeijer, Andrés Felipe Cruz-Salinas, Patrick Schramowski, Kristian Kersting, Samuel Weinbach

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

M-VADER: A Model for Diffusion with Multimodal Context.

Samuel Weinbach, Marco Bellagente, Constantin Eichenberg, Andrew Dai, Robert Baldock, Souradeep Nanda, Björn Deiseroth, Koen Oostermeijer, Hannah Teufel, Andrés Felipe Cruz-Salinas

CoRR, 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, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach

CoRR, 2022

MAGMA - Multimodal Augmentation of Generative Models through Adapter-based Finetuning.

Constantin Eichenberg, Sidney Black, Samuel Weinbach, Letitia Parcalabescu, Anette Frank

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

2020

Domain-Level Explainability - A Challenge for Creating Trust in Superhuman AI Strategies.

Jonas Andrulis, Ole Meyer, Grégory Schott, Samuel Weinbach, Volker Gruhn

CoRR, 2020