Search Results for author: Jilong Xue

Found 6 papers, 3 papers with code

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

2 code implementations27 Feb 2024 Shuming Ma, Hongyu Wang, Lingxiao Ma, Lei Wang, Wenhui Wang, Shaohan Huang, Li Dong, Ruiping Wang, Jilong Xue, Furu Wei

Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs).

Retentive Network: A Successor to Transformer for Large Language Models

8 code implementations17 Jul 2023 Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, Furu Wei

In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance.

Language Modelling

FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement

no code implementations8 Apr 2023 Xiaonan Nie, Xupeng Miao, Zilong Wang, Zichao Yang, Jilong Xue, Lingxiao Ma, Gang Cao, Bin Cui

We first present an empirical analysis on the problems and opportunities of training MoE models, which motivates us to overcome the routing imbalance and fluctuation problems by a dynamic expert management and device placement mechanism.


Towards Efficient Large-Scale Graph Neural Network Computing

no code implementations19 Oct 2018 Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, Yafei Dai

This evolution has led to large graph-based irregular and sparse models that go beyond what existing deep learning frameworks are designed for.

Graph Neural Network graph partitioning +1

RPC Considered Harmful: Fast Distributed Deep Learning on RDMA

no code implementations22 May 2018 Jilong Xue, Youshan Miao, Cheng Chen, Ming Wu, Lintao Zhang, Lidong Zhou

Its computation is typically characterized by a simple tensor data abstraction to model multi-dimensional matrices, a data-flow graph to model computation, and iterative executions with relatively frequent synchronizations, thereby making it substantially different from Map/Reduce style distributed big data computation.

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