其他

moe-training

orchestra-research/ai-research-skills

Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.

  • 971 次安装
  • 1.4万 星标
  • 收录于

安装命令

npx skills add https://github.com/orchestra-research/ai-research-skills --skill moe-training

SKILL.md

MoE Training: Mixture of Experts

When to Use This Skill

Use MoE Training when you need to:

  • Train larger models with limited compute (5× cost reduction vs dense models)
  • Scale model capacity without proportional compute increase
  • Achieve better performance per compute budget than dense models
  • Specialize experts for different domains/tasks/languages
  • Reduce inference latency with sparse activation (only 13B/47B params active in Mixtral)
  • Implement SOTA models like Mixtral 8x7B, DeepSeek-V3, Switch Transformers

Notable MoE Models: Mixtral 8x7B (Mistral AI), DeepSeek-V3, Switch Transformers (Google), GLaM (Google), NLLB-MoE (Meta)

Installation

# DeepSpeed with MoE support
pip install deepspeed>=0.6.0