moe-training SKILL.md skillA
moe-training from orchestra-research/ai-research-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add orchestra-research/ai-research-skills/moe-training@v1.0.0Pins v1.0.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- orchestra-research/ai-research-skills
- Path
19-emerging-techniques/moe-training/SKILL.md- Versions recorded
- 5
- Latest version
- v1.0.0, committed Nov 20, 2025
- First version recorded
- Nov 19, 2025
- Size
- 14,968 bytes
- Repository stars
- 13,718
- License
- MIT
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.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# 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 Transformers (Google), GLaM (Google), NLLB-MoE (Meta)
## Installation
```bash
# DeepSpeed with MoE support
pip install deepspeed>=0.6.0
# Megatron-DeepSpeed for large-scale training
git clone https://github.com/microsoft/Megatron-DeepSpeed
cd Megatron-DeepSpeed
pip install -r requirements.txt
# Alternative: HuggingFace Transformers
pip install transformers accelerate
```
## Quick Start
### Basic MoE Architecture
```python
import torch
import torch.nn as nn
class MoELayer(nn.Module):
"""Sparse Mixture of Experts layer."""
…Read the whole file at its exact version.
How to install
mdr add orchestra-research/ai-research-skills/moe-training@sha256:f13f184fe031c847The line at the top of this page pins the label v1.0.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_qxyjro5ya5734no4)
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Versions
The registry has recorded 5 versions of this file, between Nov 19, 2025 and Nov 20, 2025, more than one a day.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0.0 latest | 2025-11-20 | 0a9ded4 | 14,968 B | A | view · diff |
| v1.0.0 | 2025-11-20 | 51fc3a3 | 14,947 B | A | view · diff |
| v1.0.0 | 2025-11-20 | 81c38fe | 14,946 B | A | view · diff |
| v1.0.0 | 2025-11-19 | bd4461f | 14,945 B | A | view · diff |
| git:20251119.66e31d2 | 2025-11-19 | 66e31d2 | 14,689 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (14968 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
orchestra-research/ai-research-skills · 13,718 stars · license MIT · pushed 2026-10-11 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_qxyjro5ya5734no4 GET https://markdownregistry.com/api/v1/resolve?ref=orchestra-research/ai-research-skills/moe-training GET https://markdownregistry.com/api/v1/blob/f13f184fe031c84700d72c15dd90161a96f3b48ab6b50b250e6b86cfc4c87354
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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