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Mixture Of Experts

Mixture Of Experts is a Python/PyTorch library implementing sparsely gated Mixture of Experts layers for language models. It provides standard and two-level hierarchical configurations, with support for custom expert networks.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Mixture Of Experts — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a PyTorch model project needs a documented starting point for sparse expert layers and the developer can integrate the layer into their own model workflow.

Who it is for

  • PyTorch developers building or adapting language-model architectures.
  • Researchers prototyping sparse expert and conditional-computation designs.

Documented uses

  • Add sparsely gated expert layers to a language-model project.
  • Explore increased parameter capacity while targeting constant computation.
  • Configure hierarchical experts or supply a custom expert network.
Before you choose

Requirements & limits

The repository is public and MIT-licensed. Its README documents installation through pip.

  • The implementation targets increased language-model parameter capacity while keeping computation constant.
  • The README includes both a single-layer MoE and a two-level hierarchical example.
  • The project README recommends the separate ST Mixture of Experts implementation for current use.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the library and prepare a basic MoE layer

Set up the documented package and configure a sparsely gated layer for a PyTorch model.

Before you start

  • Python environment with PyTorch available.

Steps

  1. Install the package with the command below.

    Documented command
    pip install mixture_of_experts

  2. Import MoE from mixture_of_experts and configure it with the model dimension and desired number of experts.

  3. Review the README example's options for hidden dimension, activation, second-expert policy, capacity factors and auxiliary loss.

Expected outcome

A configured MoE layer ready to be integrated into a model; the README demonstrates an input-to-output call that also returns auxiliary loss.

Guide limitations

  • The README documents example usage but does not provide a complete language-model training workflow.
  • The README recommends using the separate ST Mixture of Experts project.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
lucidrains/mixture-of-experts · Official homepage title · retained in its source language
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