---
title: Train@home
emoji: 🚂
colorFrom: blue
colorTo: blue
sdk: static
pinned: false
---

# Train@home

Community-owned AI training. Like Folding@home, but for neural networks.

## What is this?

A distributed, open-source project where volunteers contribute compute to train language models. Anyone with a GPU can participate. The model and knowledge banks are collectively owned by all contributors.

## How it works

1. **Contribute compute** — download a checkpoint, train on your assigned data shard, upload gradients
2. **Contribute knowledge** — process documents into facts, submit them to the shared knowledge bank
3. **Govern** — propose and vote on training objectives, data distribution, architecture changes

## Current status

🚧 **Early stage** — Building the foundation. Architecture design, training infrastructure, and initial knowledge bank.

## Get involved

- Fork the main repo
- Run the setup script
- Join the discussions
- Start contributing

## Architecture

- **Processing core** — Recurrent architecture with adaptive computation and revision
- **Knowledge bank** — Hot-swappable, product-quantized fact store
- **Retrieval interface** — Always-on, integrated into the forward pass (not tool calling)

## Training cycle

Training runs happen every 2-3 months. Between runs, the community proposes changes via PRs, discusses tradeoffs, and prepares data. Config freezes 2 weeks before training starts.

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*Built by the community, for the community.*