47 citations · 138 across the 18 of their papers we have counts for
5 papers · 1 filter
Distributed Inference and Fine-tuning of Large Language Models Over The Internet
Alexander Borzunov, Max Ryabinin, Artem Chumachenko +5
Large language models (LLMs) are useful in many NLP tasks and become more capable with size, with the best open-source models having over 50 billion parameters. However, using thes…
Hypernymy Understanding Evaluation of Text-to-Image Models via WordNet Hierarchy
Anton Baryshnikov, Max Ryabinin
Text-to-image synthesis has recently attracted widespread attention due to rapidly improving quality and numerous practical applications. However, the language understanding capabi…
FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU
Ying Sheng, Lianmin Zheng, Binhang Yuan +11
The high computational and memory requirements of large language model (LLM) inference make it feasible only with multiple high-end accelerators. Motivated by the emerging demand f…
Is This Loss Informative? Faster Text-to-Image Customization by Tracking Objective Dynamics
Anton Voronov, Mikhail Khoroshikh, Artem Babenko +1
Text-to-image generation models represent the next step of evolution in image synthesis, offering a natural way to achieve flexible yet fine-grained control over the result. One em…
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient
Max Ryabinin, Tim Dettmers, Michael Diskin +1
Many deep learning applications benefit from using large models with billions of parameters. Training these models is notoriously expensive due to the need for specialized HPC clus…