2 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
Reasoning Planning for Language Models
Bao Nguyen, Hieu Trung Nguyen, Ruifeng She +2
Selecting an appropriate reasoning method for a given query remains a key challenge in language model generation. Existing approaches typically generate multiple candidate response…
Automatic Operator-level Parallelism Planning for Distributed Deep Learning -- A Mixed-Integer Programming Approach
Ruifeng She, Bowen Pang, Kai Li +2
As the artificial intelligence community advances into the era of large models with billions of parameters, distributed training and inference have become essential. While various…
MoLAE: Mixture of Latent Experts for Parameter-Efficient Language Models
Zehua Liu, Han Wu, Ruifeng She +4
Mixture of Experts (MoE) has become a key architectural paradigm for efficiently scaling Large Language Models (LLMs) by selectively activating a subset of parameters for each inpu…
Hybrid Offline-online Scheduling Method for Large Language Model Inference Optimization
Bowen Pang, Kai Li, Ruifeng She +1
With the development of large language models (LLMs), it has become increasingly important to optimize hardware usage and improve throughput. In this paper, we study the inference…
LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging
Zehua Liu, Han Wu, Yuxuan Yao +4
While most current approaches rely on further training techniques, such as fine-tuning or reinforcement learning, to enhance model capacities, model merging stands out for its abil…