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| 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. | |
| --- | |
| *Built by the community, for the community.* |