What problem does it solve?
MoE Training helps you build and optimize sparse expert-based models when dense training is too expensive, enabling far more model capacity without proportional compute growth.
Core Features & Use Cases
- Routing and Expert Design: Configure top-1, top-2, or expert-choice routing patterns for architectures like Switch, Mixtral, and DeepSeek.
- Load Balancing and Stability: Tune auxiliary loss, router z-loss, capacity factors, and expert parallelism to keep training efficient and stable.
- Production Training and Inference: Use the included guidance to plan large-scale training runs, prune or distribute experts, and deploy sparse inference efficiently.
- Use Case: A team building a 47B-style Mixtral variant can use this Skill to pick expert counts, set routing rules, and choose the right DeepSpeed configuration for the available GPUs.
Quick Start
Use the moe-training skill to design a sparse MoE training plan for your model, including expert count, routing strategy, capacity settings, and distributed training configuration.