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Tim Dettmers
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2020 – today
- 2024
- [c16]Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh:
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. ICLR 2024 - [i18]Rulin Shao, Jacqueline He, Akari Asai, Weijia Shi, Tim Dettmers, Sewon Min, Luke Zettlemoyer, Pang Wei Koh:
Scaling Retrieval-Based Language Models with a Trillion-Token Datastore. CoRR abs/2407.12854 (2024) - 2023
- [c15]Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Maksim Riabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, Colin Raffel:
Petals: Collaborative Inference and Fine-tuning of Large Models. ACL (demo) 2023: 558-568 - [c14]Zeyu Liu, Tim Dettmers, Xi Lin, Veselin Stoyanov, Xian Li:
Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model. EMNLP 2023: 15038-15061 - [c13]Tim Dettmers, Luke Zettlemoyer:
The case for 4-bit precision: k-bit Inference Scaling Laws. ICML 2023: 7750-7774 - [c12]Max Ryabinin, Tim Dettmers, Michael Diskin, Alexander Borzunov:
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient. ICML 2023: 29416-29440 - [c11]Alexander Borzunov, Max Ryabinin, Artem Chumachenko, Dmitry Baranchuk, Tim Dettmers, Younes Belkada, Pavel Samygin, Colin A. Raffel:
Distributed Inference and Fine-tuning of Large Language Models Over The Internet. NeurIPS 2023 - [c10]Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer:
QLoRA: Efficient Finetuning of Quantized LLMs. NeurIPS 2023 - [c9]Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos, Ali Farhadi, Ludwig Schmidt:
Stable and low-precision training for large-scale vision-language models. NeurIPS 2023 - [i17]Max Ryabinin, Tim Dettmers, Michael Diskin, Alexander Borzunov:
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient. CoRR abs/2301.11913 (2023) - [i16]Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos, Ali Farhadi, Ludwig Schmidt:
Stable and low-precision training for large-scale vision-language models. CoRR abs/2304.13013 (2023) - [i15]Leo Z. Liu, Tim Dettmers, Xi Victoria Lin, Veselin Stoyanov, Xian Li:
Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model. CoRR abs/2305.13999 (2023) - [i14]Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer:
QLoRA: Efficient Finetuning of Quantized LLMs. CoRR abs/2305.14314 (2023) - [i13]Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh:
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. CoRR abs/2306.03078 (2023) - [i12]Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, Kaifeng Chen, Inderjit S. Dhillon, Yulia Tsvetkov, Hannaneh Hajishirzi, Sham M. Kakade, Ali Farhadi, Prateek Jain:
MatFormer: Nested Transformer for Elastic Inference. CoRR abs/2310.07707 (2023) - [i11]Alexander Borzunov, Max Ryabinin, Artem Chumachenko, Dmitry Baranchuk, Tim Dettmers, Younes Belkada, Pavel Samygin, Colin Raffel:
Distributed Inference and Fine-tuning of Large Language Models Over The Internet. CoRR abs/2312.08361 (2023) - 2022
- [c8]Tim Dettmers, Mike Lewis, Sam Shleifer, Luke Zettlemoyer:
8-bit Optimizers via Block-wise Quantization. ICLR 2022 - [c7]Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer:
GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale. NeurIPS 2022 - [i10]Alexander Borzunov, Max Ryabinin, Tim Dettmers, Quentin Lhoest, Lucile Saulnier, Michael Diskin
, Yacine Jernite, Thomas Wolf:
Training Transformers Together. CoRR abs/2207.03481 (2022) - [i9]Margaret Li, Suchin Gururangan, Tim Dettmers, Mike Lewis, Tim Althoff, Noah A. Smith, Luke Zettlemoyer:
Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models. CoRR abs/2208.03306 (2022) - [i8]Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer:
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale. CoRR abs/2208.07339 (2022) - [i7]Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Max Ryabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, Colin Raffel:
Petals: Collaborative Inference and Fine-tuning of Large Models. CoRR abs/2209.01188 (2022) - [i6]Tim Dettmers, Luke Zettlemoyer:
The case for 4-bit precision: k-bit Inference Scaling Laws. CoRR abs/2212.09720 (2022) - 2021
- [c6]Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, Luke Zettlemoyer:
BASE Layers: Simplifying Training of Large, Sparse Models. ICML 2021: 6265-6274 - [c5]Alexander Borzunov, Max Ryabinin, Tim Dettmers, Quentin Lhoest, Lucile Saulnier, Michael Diskin, Yacine Jernite, Thomas Wolf:
Training Transformers Together. NeurIPS (Competition and Demos) 2021: 335-342 - [i5]Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, Luke Zettlemoyer:
BASE Layers: Simplifying Training of Large, Sparse Models. CoRR abs/2103.16716 (2021) - [i4]Tim Dettmers, Mike Lewis, Sam Shleifer, Luke Zettlemoyer:
8-bit Optimizers via Block-wise Quantization. CoRR abs/2110.02861 (2021) - 2020
- [c4]Gabriel Ilharco, Cesar Ilharco, Iulia Turc, Tim Dettmers, Felipe Ferreira, Kenton Lee:
High Performance Natural Language Processing. EMNLP (Tutorial Abstracts) 2020: 24-27
2010 – 2019
- 2019
- [i3]Tim Dettmers, Luke Zettlemoyer:
Sparse Networks from Scratch: Faster Training without Losing Performance. CoRR abs/1907.04840 (2019) - 2018
- [c3]Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel:
Convolutional 2D Knowledge Graph Embeddings. AAAI 2018: 1811-1818 - [c2]Dirk Weissenborn, Pasquale Minervini, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bosnjak, Jeff Mitchell, Thomas Demeester, Tim Dettmers, Pontus Stenetorp, Sebastian Riedel:
Jack the Reader - A Machine Reading Framework. ACL (4) 2018: 25-30 - [i2]Dirk Weissenborn, Pasquale Minervini, Tim Dettmers, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bosnjak, Jeff Mitchell, Thomas Demeester, Pontus Stenetorp, Sebastian Riedel:
Jack the Reader - A Machine Reading Framework. CoRR abs/1806.08727 (2018) - 2017
- [i1]Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel:
Convolutional 2D Knowledge Graph Embeddings. CoRR abs/1707.01476 (2017) - 2016
- [c1]Tim Dettmers:
8-Bit Approximations for Parallelism in Deep Learning. ICLR (Poster) 2016
Coauthor Index
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last updated on 2024-08-23 19:21 CEST by the dblp team
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