Tim Dettmers

According to our database, Tim Dettmers authored at least 22 papers between 2016 and 2023.

Collaborative distances:

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

2023

MatFormer: Nested Transformer for Elastic Inference.

Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, Kaifeng Chen, Inderjit S. Dhillon, Yulia Tsvetkov, Hannaneh Hajishirzi, Sham M. Kakade, Ali Farhadi, Prateek Jain

CoRR, 2023

SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.

Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh

CoRR, 2023

Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model.

Leo Z. Liu, Tim Dettmers, Xi Victoria Lin, Veselin Stoyanov, Xian Li

CoRR, 2023

Stable and low-precision training for large-scale vision-language models.

Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos, Ali Farhadi, Ludwig Schmidt

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

QLoRA: Efficient Finetuning of Quantized LLMs.

Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer

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

Distributed Inference and Fine-tuning of Large Language Models Over The Internet.

Alexander Borzunov, Max Ryabinin, Artem Chumachenko, Dmitry Baranchuk, Tim Dettmers, Younes Belkada, Pavel Samygin, Colin A. Raffel

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

SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient.

Max Ryabinin, Tim Dettmers, Michael Diskin, Alexander Borzunov

Proceedings of the International Conference on Machine Learning, 2023

The case for 4-bit precision: k-bit Inference Scaling Laws.

Tim Dettmers, Luke Zettlemoyer

Proceedings of the International Conference on Machine Learning, 2023

Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model.

Zeyu Liu, Tim Dettmers, Xi Lin, Veselin Stoyanov, Xian Li

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Petals: Collaborative Inference and Fine-tuning of Large Models.

Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Maksim Riabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, Colin Raffel

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2023

2022

Petals: Collaborative Inference and Fine-tuning of Large Models.

Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Max Ryabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, Colin Raffel

CoRR, 2022

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale.

Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer

CoRR, 2022

Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models.

Margaret Li, Suchin Gururangan, Tim Dettmers, Mike Lewis, Tim Althoff, Noah A. Smith, Luke Zettlemoyer

CoRR, 2022

GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale.

Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

8-bit Optimizers via Block-wise Quantization.

Tim Dettmers, Mike Lewis, Sam Shleifer, Luke Zettlemoyer

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Training Transformers Together.

Alexander Borzunov, Max Ryabinin, Tim Dettmers, Quentin Lhoest, Lucile Saulnier, Michael Diskin, Yacine Jernite, Thomas Wolf

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

BASE Layers: Simplifying Training of Large, Sparse Models.

Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, Luke Zettlemoyer

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

High Performance Natural Language Processing.

Gabriel Ilharco, Cesar Ilharco, Iulia Turc, Tim Dettmers, Felipe Ferreira, Kenton Lee

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, 2020

2019

Sparse Networks from Scratch: Faster Training without Losing Performance.

Tim Dettmers, Luke Zettlemoyer

CoRR, 2019

2018

Jack the Reader - A Machine Reading Framework.

Dirk Weissenborn, Pasquale Minervini, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bosnjak, Jeff Mitchell, Thomas Demeester, Tim Dettmers, Pontus Stenetorp, Sebastian Riedel

Proceedings of ACL 2018, Melbourne, Australia, July 15-20, 2018, System Demonstrations, 2018

Convolutional 2D Knowledge Graph Embeddings.

Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2016

8-Bit Approximations for Parallelism in Deep Learning.

Tim Dettmers

Proceedings of the 4th International Conference on Learning Representations, 2016