MoE Training
Train Mixture-of-Experts models with DeepSpeed or HuggingFace — routing, load balancing, expert parallelism
- What
- Train Mixture-of-Experts models with DeepSpeed or HuggingFace — routing, load balancing, expert parallelism
- Cost
- Free
- Needs
- none to install — no account, no API keys. To actually train MoE models you need serious multi-GPU hardware, Python, and the listed dependencies (deepspeed, transformers, torch, accelerate).
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — a hands-on skill for training Mixture-of-Experts models: MoE architecture and top-k routing mechanisms, load balancing (auxiliary loss, router z-loss), expert parallelism with DeepSpeed, capacity-factor tuning, learning-rate guidelines specific to MoE (lower than dense models, longer decay), plus working PyTorch examples from a basic MoELayer to a Mixtral-style block, training scripts, inference optimization with sparse activation, and common pitfalls. Covers the notable MoE family — Mixtral 8x7B, DeepSeek-V3, Switch Transformers, GLaM. Discovered via skills.sh, listed here with credit to its creator by @ovachiever. Honest caveats: real MoE training needs serious multi-GPU hardware — the code is a guide, not a turnkey training stack; declares MIT in its frontmatter but the license file still needs checking; fast-moving field — verify current DeepSpeed/transformers versions before running anything. Skill Harbor never reviews the code, review it yourself before use. Not verified.
Version:
Install
Prerequisites: none to install — no account, no API keys. To actually train MoE models you need serious multi-GPU hardware, Python, and the listed dependencies (deepspeed, transformers, torch, accelerate). Install "MoE Training" for me. A hands-on skill for training Mixture-of-Experts models: MoE architecture, top-k routing, load balancing, expert parallelism with DeepSpeed, tuning guidelines, working PyTorch examples and training scripts. Repository: https://github.com/ovachiever/droid-tings/blob/master/skills/moe-training/SKILL.md 1. Fetch the SKILL.md file for the moe-training skill (and its reference files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code, credentials only via the secure vault, allowed hosts declared in the SKILL.md. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "moe-training". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. install the dependencies and provision GPU hardware for real training). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.