Dettmers, Tim

QLoRA: Efficient Finetuning of Quantized LLMs

Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 3,832

Convolutional 2D Knowledge Graph Embeddings

Dettmers, T., Minervini, P., Stenetorp, P., & Riedel, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Advanced Graph Neural NetworksTopic Modeling

Cited by 2,486

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

Dettmers, T., Lewis, M., Belkada, Y., & Zettlemoyer, L. (2022) · arXiv

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 305

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

Dettmers, T., Lewis, M., Belkada, Y., & Zettlemoyer, L. (2022) · Advances in Neural Information Processing Systems 35

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 305

BASE Layers: Simplifying Training of Large, Sparse Models

Lewis, M., Bhosale, S., Dettmers, T., Goyal, N., & Zettlemoyer, L. (2021) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 88

Petals: Collaborative Inference and Fine-tuning of Large Models

Borzunov, A., Baranchuk, D., Dettmers, T., Riabinin, M., Belkada, Y., Chumachenko, A., Samygin, P., & Raffel, C. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

Topic ModelingNatural Language Processing Techniques

Cited by 52

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

Dettmers, T., & Zettlemoyer, L. (2022) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 46

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

Borzunov, A., Ryabinin, M., Chumachenko, A., Baranchuk, D., Dettmers, T., Belkada, Y., Samygin, P., & Raffel, C. (2023) · arXiv

Topic ModelingPrivacy-Preserving Technologies in Data

Cited by 35

Convolutional 2D Knowledge Graph Embeddings

Dettmers, T., Minervini, P., Stenetorp, P., & Riedel, S. (2017) · arXiv

Advanced Graph Neural NetworksTopic Modeling

Cited by 30

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

Ryabinin, M., Dettmers, T., Diskin, M., & Borzunov, A. (2023) · arXiv

Parallel Computing and Optimization TechniquesStochastic Gradient Optimization Techniques

Cited by 8

Scaling Retrieval-Based Language Models with a Trillion-Token Datastore

Shao, R., He, J., Asai, A., Shi, W., Dettmers, T., Min, S., Zettlemoyer, L., & Koh, P. (2024) · Advances in Neural Information Processing Systems 37

Topic ModelingInformation Retrieval and Search Behavior

Cited by 8

Petals: Collaborative Inference and Fine-tuning of Large Models

Borzunov, A., Baranchuk, D., Dettmers, T., Ryabinin, M., Belkada, Y., Chumachenko, A., Samygin, P., & Raffel, C. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 6

SERA: Soft-Verified Efficient Repository Agents

Shen, E., Tormoen, D., Shah, S., Farhadi, A., & Dettmers, T. (2026) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 0