no code implementations • 14 Mar 2024 • Piotr Nawrot, Adrian Łańcucki, Marcin Chochowski, David Tarjan, Edoardo M. Ponti
Transformers have emerged as the backbone of large language models (LLMs).
1 code implementation • 5 Sep 2023 • Piotr Nawrot
With the introduction of this open-source framework, we hope to widen the accessibility to language modelling research and cater to the community's demand for more user-friendly T5 (Encoder-Decoder) implementations.
1 code implementation • NeurIPS 2023 • Jean Kaddour, Oscar Key, Piotr Nawrot, Pasquale Minervini, Matt J. Kusner
The computation necessary for training Transformer-based language models has skyrocketed in recent years.
1 code implementation • 17 Nov 2022 • Piotr Nawrot, Jan Chorowski, Adrian Łańcucki, Edoardo M. Ponti
Transformers achieve unrivalled performance in modelling language, but remain inefficient in terms of memory and time complexity.
3 code implementations • Findings (NAACL) 2022 • Piotr Nawrot, Szymon Tworkowski, Michał Tyrolski, Łukasz Kaiser, Yuhuai Wu, Christian Szegedy, Henryk Michalewski
Transformer models yield impressive results on many NLP and sequence modeling tasks.
Ranked #7 on
Image Generation
on ImageNet 32x32
(bpd metric)