4 papers
LoRAFusion: Efficient LoRA Fine-Tuning for LLMs
Zhanda Zhu, Qidong Su, Yaoyao Ding +3
Low-Rank Adaptation (LoRA) has become the leading Parameter-Efficient Fine-Tuning (PEFT) method for Large Language Models (LLMs), as it significantly reduces GPU memory usage while…
Aegis: Taxonomy and Optimizations for Overcoming Agent-Environment Failures in LLM Agents
Kevin Song, Anand Jayarajan, Yaoyao Ding +4
Large Language Models (LLMs) agents augmented with domain tools promise to autonomously execute complex tasks requiring human-level intelligence, such as customer service and digit…
Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization
Zhanda Zhu, Christina Giannoula, Muralidhar Andoorveedu +4
Various parallelism, such as data, tensor, and pipeline parallelism, along with memory optimizations like activation checkpointing, redundancy elimination, and offloading, have bee…
Seesaw: High-throughput LLM Inference via Model Re-sharding
Qidong Su, Wei Zhao, Xin Li +6
To improve the efficiency of distributed large language model (LLM) inference, various parallelization strategies, such as tensor and pipeline parallelism, have been proposed. Howe…