moe-training
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.
Install
npx skills add davila7/claude-code-templates@moe-trainingRuns in your terminal. Adds the skill globally for Claude Code, Cursor, Codex and others; add -g -y to skip prompts.
What it does
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…
Excerpt from the skill's own SKILL.md. Read the full file on GitHub before installing: skills run with your agent's permissions.
View source on GitHubBefore you install
Skills are plain text instructions the agent follows, sometimes with scripts. Check the source, prefer repositories with many installs and stars, and read any script it ships.
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