# flash-moe

> A Mixture of Experts (MoE) implementation in Python, enabling efficient sparse model inference by routing inputs to specialized expert sub-networks.

flash-moe is an open-source Python library implementing the Mixture of Experts (MoE) architecture, designed to enable efficient sparse model inference by dynamically routing inputs to specialized expert sub-networks. It provides a lightweight, developer-friendly interface for building and running MoE-based models, making it easier to experiment with sparse activation patterns in deep learning. The project is hosted on GitHub and is available for direct use or integration into larger ML pipelines.

- **Mixture of Experts Architecture**: *Implements sparse MoE routing so only a subset of expert networks are activated per input, reducing compute costs.*
- **Python-native**: *Written in Python for easy integration with existing ML workflows and frameworks.*
- **Open Source**: *Fully open-source on GitHub under a permissive license, allowing free use, modification, and contribution.*
- **Lightweight Design**: *Minimal dependencies and a focused codebase make it straightforward to embed in research or production projects.*
- **Developer-Friendly**: *Clone the repository, install dependencies, and start experimenting with MoE models immediately.*

## Features
- Mixture of Experts (MoE) routing
- Sparse model inference
- Python-native implementation
- Open-source codebase
- Lightweight and minimal dependencies

## Integrations
Python

## Platforms
WEB, API, DEVELOPER_SDK, CLI

## Pricing
Open Source

## Links
- Website: https://github.com/danveloper/flash-moe
- Documentation: https://github.com/danveloper/flash-moe/blob/main/README.md
- Repository: https://github.com/danveloper/flash-moe
- EveryDev.ai: https://www.everydev.ai/tools/flash-moe