Tim Dettmers - RankMe

42 papers A* 16Journal 21Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	SERA: Soft-Verified Efficient Repository Agents. CoRR Ethan Shen, Danny Tormoen, Saurabh Shah, Ali Farhadi, Tim Dettmers
2025	A*	`conf`	Holistically Evaluating the Environmental Impact of Creating Language Models. ICLR Jacob Morrison, Clara Na, Jared Fernandez, Tim Dettmers, Emma Strubell, Jesse Dodge
2025	J	`jnl`	Holistically Evaluating the Environmental Impact of Creating Language Models. CoRR Jacob Morrison, Clara Na, Jared Fernandez, Tim Dettmers, Emma Strubell, Jesse Dodge
2025	A*	`conf`	OLMoE: Open Mixture-of-Experts Language Models. ICLR Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Jacob Morrison, Sewon Min, Weijia Shi, Evan Pete Walsh, Oyvind Tafjord, Nathan Lambert, Yuling Gu, Shane Arora, Akshita Bhagia, Dustin Schwenk, David Wadden, Alexander Wettig, Binyuan Hui, Tim Dettmers, Douwe Kiela, Ali Farhadi, et al.
2024	—	`—`	Accessible Foundation Models: Systems, Algorithms, and Science Tim Dettmers
2024	A*	`conf`	MatFormer: Nested Transformer for Elastic Inference. NeurIPS Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, Kaifeng Chen, Inderjit S. Dhillon, Yulia Tsvetkov, Hanna Hajishirzi, Sham M. Kakade, Ali Farhadi, Prateek Jain
2024	J	`jnl`	OLMoE: Open Mixture-of-Experts Language Models. CoRR Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Jacob Morrison, Sewon Min, Weijia Shi, Pete Walsh, Oyvind Tafjord, Nathan Lambert, Yuling Gu, Shane Arora, Akshita Bhagia, Dustin Schwenk, David Wadden, Alexander Wettig, Binyuan Hui, Tim Dettmers, Douwe Kiela, Ali Farhadi, Noah A. Smith, Pang Wei Koh, Amanpreet Singh, Hannaneh Hajishirzi
2024	A*	`conf`	Scaling Retrieval-Based Language Models with a Trillion-Token Datastore. NeurIPS Rulin Shao, Jacqueline He, Akari Asai, Weijia Shi, Tim Dettmers, Sewon Min, Luke Zettlemoyer, Pang Wei Koh
2024	J	`jnl`	Scaling Retrieval-Based Language Models with a Trillion-Token Datastore. CoRR Rulin Shao, Jacqueline He, Akari Asai, Weijia Shi, Tim Dettmers, Sewon Min, Luke Zettlemoyer, Pang Wei Koh
2024	A*	`conf`	SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. ICLR Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh
2023	A*	`conf`	Distributed Inference and Fine-tuning of Large Language Models Over The Internet. NeurIPS Alexander Borzunov, Max Ryabinin, Artem Chumachenko, Dmitry Baranchuk, Tim Dettmers, Younes Belkada, Pavel Samygin, Colin A. Raffel
2023	J	`jnl`	Distributed Inference and Fine-tuning of Large Language Models Over The Internet. CoRR Alexander Borzunov, Max Ryabinin, Artem Chumachenko, Dmitry Baranchuk, Tim Dettmers, Younes Belkada, Pavel Samygin, Colin Raffel
2023	J	`jnl`	MatFormer: Nested Transformer for Elastic Inference. CoRR Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, Kaifeng Chen, Inderjit S. Dhillon, Yulia Tsvetkov, Hannaneh Hajishirzi, Sham M. Kakade, Ali Farhadi, Prateek Jain
2023	—	`conf`	Petals: Collaborative Inference and Fine-tuning of Large Models. ACL (demo) Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Maksim Riabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, Colin Raffel
2023	A*	`conf`	QLoRA: Efficient Finetuning of Quantized LLMs. NeurIPS Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer
2023	J	`jnl`	QLoRA: Efficient Finetuning of Quantized LLMs. CoRR Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer
2023	A*	`conf`	SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient. ICML Max Ryabinin, Tim Dettmers, Michael Diskin, Alexander Borzunov
2023	J	`jnl`	SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient. CoRR Max Ryabinin, Tim Dettmers, Michael Diskin, Alexander Borzunov
2023	J	`jnl`	SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. CoRR Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh
2023	A*	`conf`	Stable and low-precision training for large-scale vision-language models. NeurIPS Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos, Ali Farhadi, Ludwig Schmidt
2023	J	`jnl`	Stable and low-precision training for large-scale vision-language models. CoRR Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos, Ali Farhadi, Ludwig Schmidt
2023	A*	`conf`	The case for 4-bit precision: k-bit Inference Scaling Laws. ICML Tim Dettmers, Luke Zettlemoyer
2023	A*	`conf`	Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model. EMNLP Zeyu Liu, Tim Dettmers, Xi Lin, Veselin Stoyanov, Xian Li
2023	J	`jnl`	Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model. CoRR Leo Z. Liu, Tim Dettmers, Xi Victoria Lin, Veselin Stoyanov, Xian Li
2022	A*	`conf`	8-bit Optimizers via Block-wise Quantization. ICLR Tim Dettmers, Mike Lewis, Sam Shleifer, Luke Zettlemoyer
2022	J	`jnl`	Branch-Train-Merge: Embarrassingly Parallel Training of Expert Language Models. CoRR Margaret Li, Suchin Gururangan, Tim Dettmers, Mike Lewis, Tim Althoff, Noah A. Smith, Luke Zettlemoyer
2022	A*	`conf`	GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale. NeurIPS Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer
2022	J	`jnl`	LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale. CoRR Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer
2022	J	`jnl`	Petals: Collaborative Inference and Fine-tuning of Large Models. CoRR Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Max Ryabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, Colin Raffel
2022	J	`jnl`	The case for 4-bit precision: k-bit Inference Scaling Laws. CoRR Tim Dettmers, Luke Zettlemoyer
2022	J	`jnl`	Training Transformers Together. CoRR Alexander Borzunov, Max Ryabinin, Tim Dettmers, Quentin Lhoest, Lucile Saulnier, Michael Diskin, Yacine Jernite, Thomas Wolf
2021	J	`jnl`	8-bit Optimizers via Block-wise Quantization. CoRR Tim Dettmers, Mike Lewis, Sam Shleifer, Luke Zettlemoyer
2021	A*	`conf`	BASE Layers: Simplifying Training of Large, Sparse Models. ICML Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, Luke Zettlemoyer
2021	J	`jnl`	BASE Layers: Simplifying Training of Large, Sparse Models. CoRR Mike Lewis, Shruti Bhosale, Tim Dettmers, Naman Goyal, Luke Zettlemoyer
2021	—	`conf`	Training Transformers Together. NeurIPS (Competition and Demos) Alexander Borzunov, Max Ryabinin, Tim Dettmers, Quentin Lhoest, Lucile Saulnier, Michael Diskin, Yacine Jernite, Thomas Wolf
2020	—	`conf`	High Performance Natural Language Processing. EMNLP (Tutorial Abstracts) Gabriel Ilharco, Cesar Ilharco, Iulia Turc, Tim Dettmers, Felipe Ferreira, Kenton Lee
2019	J	`jnl`	Sparse Networks from Scratch: Faster Training without Losing Performance. CoRR Tim Dettmers, Luke Zettlemoyer
2018	A*	`conf`	Convolutional 2D Knowledge Graph Embeddings. AAAI Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel
2018	—	`conf`	Jack the Reader - A Machine Reading Framework. ACL (4) Dirk Weissenborn, Pasquale Minervini, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bosnjak, Jeff Mitchell, Thomas Demeester, Tim Dettmers, Pontus Stenetorp, Sebastian Riedel
2018	J	`jnl`	Jack the Reader - A Machine Reading Framework. CoRR Dirk Weissenborn, Pasquale Minervini, Tim Dettmers, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bosnjak, Jeff Mitchell, Thomas Demeester, Pontus Stenetorp, Sebastian Riedel
2017	J	`jnl`	Convolutional 2D Knowledge Graph Embeddings. CoRR Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel
2016	A*	`conf`	8-Bit Approximations for Parallelism in Deep Learning. ICLR (Poster) Tim Dettmers