Train Mixture-of-Experts models at scale with DeepSpeed or HuggingFace.
npx skills add https://github.com/OpenLAIR/dr-claw --skill moe-training-openlair
Please help me install this Agent Skill. Skill: moe-training Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/emerging-techniques/moe-training Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill moe-training-openlair
Train Mixture-of-Experts (MoE) models efficiently at scale.
Launch a baseline MoE training run with a small dataset on 8 experts to validate setup and performance.
High-intent search queries and answers about installing and using this skill.
To train Mixture-of-Experts models efficiently at scale, this Skill provides a baseline implementation using DeepSpeed and HuggingFace. It supports Mixtral 8x7B, DeepSeek-V3, and Switch Transformers with defined routing and expert parallelism.
Expert parallelism in sparse models distributes individual experts across multiple devices to balance computational load. This Skill implements load balancing and routing mechanisms to enable scalable capacity and reduce compute costs from 1B to 100B parameters.
Yes, you can use HuggingFace transformers with DeepSpeed for MoE training. The Skill explicitly depends on both frameworks, along with torch and accelerate, to execute production-grade training workflows with 8 to 128 experts.
You can start a baseline MoE training run by launching a small dataset configuration with 8 experts. This validates your DeepSpeed and HuggingFace environment setup and verifies performance before scaling to larger sparse model architectures.
Yes, the Skill supports inference optimization for Mixture-of-Experts architectures. It includes routing mechanisms, load balancing, and expert parallelism configurations specifically designed to streamline production-grade sparse model workflows.
Scaling sparse models from 1B to 100B parameters requires explicit dependencies on deepspeed, transformers, torch, and accelerate. The Skill addresses these constraints through defined MoE architectures and evaluation considerations for distributed training.
Yangel-hideAutomate distributed training setup across CPU, GPU, and TPU environments.
tianhao909Train Mixture of Experts models using DeepSpeed and HuggingFace Transformers.
MesferAliTrain Mixture of Experts models using DeepSpeed and HuggingFace Transformers.
informatico-madridTrain Mixture of Experts models using DeepSpeed and HuggingFace Transformers.
