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- title: README
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- emoji: πŸ“š
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- Edit this `README.md` markdown file to author your organization card.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ title: Train@home
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+ emoji: πŸš‚
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+ # Train@home
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+ Community-owned AI training. Like Folding@home, but for neural networks.
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+ ## What is this?
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+ 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.
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+ ## How it works
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+ 1. **Contribute compute** β€” download a checkpoint, train on your assigned data shard, upload gradients
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+ 2. **Contribute knowledge** β€” process documents into facts, submit them to the shared knowledge bank
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+ 3. **Govern** β€” propose and vote on training objectives, data distribution, architecture changes
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+ ## Current status
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+ 🚧 **Early stage** β€” Building the foundation. Architecture design, training infrastructure, and initial knowledge bank.
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+ ## Get involved
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+ - Fork the main repo
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+ - Run the setup script
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+ - Join the discussions
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+ - Start contributing
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+ ## Architecture
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+ - **Processing core** β€” Recurrent architecture with adaptive computation and revision
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+ - **Knowledge bank** β€” Hot-swappable, product-quantized fact store
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+ - **Retrieval interface** β€” Always-on, integrated into the forward pass (not tool calling)
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+ ## Training cycle
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+ 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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+ ---
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+ *Built by the community, for the community.*