M 模测 › 案例集 › Global-batch load balance almost free lunch to improve your MoE LLM training 返回案例集

Global-batch load balance almost free lunch to improve your MoE LLM training

GITHUB HUGGING FACE MODELSCOPE DISCORD Background The Mixture-of-Experts (MoEs) architecture has become a popular model-parameter-scale-up technique. Typically, one MoE layer consi…

2026年5月19日 125 次阅读
Qwen 通义 中文

Global-batch load balance almost free lunch to improve your MoE LLM training

来源:Qwen · 发布于 2025/1/21

GITHUB HUGGING FACE MODELSCOPE DISCORD Background The Mixture-of-Experts (MoEs) architecture has become a popular model-parameter-scale-up technique. Typically, one MoE layer consists of a router (often parameterized as one single Linear layer) and a group of experts (for transformer-based models, each expert is one feedforward layer). Given an input, only a subset of experts will be activated, and then their outputs will be aggregated based on the scores the router assigned.

↗ 查看原文 / 完整内容
https://qwenlm.github.io/blog/global-load-balance/

© 2025 ModTst. All rights reserved. 苏ICP备2026025231号-3 苏公网安备32059002007950号