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Guide · Agent skill

How to use the MoE Training Guide agent skill (moe-training)

Updated · written with AI from the project's own documentation and checked against our catalogue data

The moe-training skill is a set of instructions and reference files that teaches an AI agent to advise on and write code for Mixture of Experts (MoE) models. An MoE model holds many specialist sub-networks but activates only a few per input, so capacity can grow without a matching rise in compute. This guide shows how to install the skill, ask for it in plain language, and what to check before you run anything. It is aimed at readers who have never used agent skills.

MoE Training GuideOrchestra-Research/AI-Research-SKILLs · MIT

Quick start

  1. 1Download the ZIP. Get the moe-training skill ZIP from the skill page.
  2. 2Unzip into a skills folder. Extract it into ~/.claude/skills/ for all projects, or into a project's .claude/skills/ folder.
  3. 3Or upload in the Claude apps. In Settings, open Capabilities and upload the skill under Skills.
  4. 4Check your environment. Confirm Python, PyTorch and DeepSpeed 0.6.0 or higher are available, plus transformers and accelerate for the HuggingFace path.
  5. 5Ask in plain language. Describe the MoE task you need, such as a routing design or a DeepSpeed config.
  6. 6Review and tune. Adapt the generated code and configs for your hardware, data and cost before running training.

What the skill makes the agent do

Once installed, the agent can explain how MoE models work and write code for them. The skill covers MoE architectures, routing mechanisms (choosing which experts handle each token), load balancing, expert parallelism, and inference tradeoffs.

The skill includes a starting-point PyTorch MoE layer and DeepSpeed training commands and configuration that you can adapt. Its reference files cover architectures (including Mixtral 8x7B, DeepSeek-V3, Switch Transformers and GLaM), training, and inference.

Install the moe-training skill

Download the ZIP from the skill page and unzip it into one of two locations. For a personal install available in all your projects, use ~/.claude/skills/. For a single project, use the .claude/skills/ folder inside that project.

If you use the Claude apps instead, open Settings, go to Capabilities, and upload the skill under Skills.

  • Personal: ~/.claude/skills/moe-training
  • Project only: .claude/skills/moe-training inside your project folder

Trigger the skill with natural requests

You do not need a special command. Describe the task in plain language that matches the skill's purpose: training large models with limited compute, sparse architectures, or scaling capacity without proportional compute.

  • Help me design a top-2 routed MoE layer for a small language model.
  • Write a DeepSpeed MoE config with 128 experts across 8 GPUs and explain each field.
  • Explain load balancing loss and how to tune its coefficient.
  • What inference tradeoffs should I expect from a Mixtral-style model?

What you need before you start

The skill's instructions assume Python and PyTorch. The DeepSpeed path needs DeepSpeed version 0.6.0 or higher. The large-scale example uses the Megatron-DeepSpeed repository, cloned from GitHub. The HuggingFace path needs transformers and accelerate. Realistic training needs multi-GPU hardware.

The skill does not run scripts or use the network itself, so the install commands it shows are for you to run.

pip install deepspeed>=0.6.0 git clone https://github.com/microsoft/Megatron-DeepSpeed cd Megatron-DeepSpeed pip install -r requirements.txt pip install transformers accelerate

Review examples before running them

The example commands and configs are illustrative. They need tuning for your hardware, data and budget. The 5x cost reduction mentioned in the skill is a general claim, not a guarantee.

Treat generated code as a draft. Check expert counts, capacity factors and batch sizes against your own GPU memory before any long run.

  • Start with a small model and few training iterations.
  • Confirm expert parallel size divides your GPU count and expert count.
  • Keep the auxiliary load balancing and z-loss terms in mind when changing them.

What people use it for

  • Drafting a starting-point MoE layer with top-k routing in PyTorch.
  • Writing a DeepSpeed MoE configuration with expert parallelism and capacity factors.
  • Explaining routing choices such as top-1, top-2 and expert choice routing.
  • Comparing sparse designs like Mixtral 8x7B and DeepSeek-V3 for a planned project.
  • Discussing inference tradeoffs such as latency and throughput for MoE models.

Frequently asked questions

Is the moe-training skill free?

The skill is published under the MIT licence, as stated on its page. Check the licence file in the repository for the full terms.

How do I install the moe-training skill?

Download the ZIP from the skill page and unzip it into ~/.claude/skills/ for personal use or a project's .claude/skills/ folder. In the Claude apps, you can instead upload it under Settings, then Capabilities, then Skills.

What do I need to use it?

You need Python and PyTorch, DeepSpeed 0.6.0 or higher for the DeepSpeed path, and transformers and accelerate for the HuggingFace path. Realistic training needs multi-GPU hardware.

Does the skill run code or access the internet?

According to the skill's listing, it does not run scripts and does not use the network. Any commands it shows, such as pip install or git clone, are for you to run yourself.

Can I use this skill instead of an API for a model?

No. It covers building and training MoE models, not calling existing models through an API. If you only need to use a hosted model, this skill adds little value.

Is the generated training config ready for production?

No. The example commands and configs are illustrative and need tuning for your hardware, data and cost, so review them before any production training run.

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Our verdict

Use moe-training if you are an ML engineer or researcher planning MoE training under a limited compute budget, or a developer who wants a starting-point MoE layer and DeepSpeed config to adapt. Pick something else if you only need to call an existing model through an API, or if you lack GPU infrastructure for training at scale.

Download the skill