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README.md
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title: Train@home
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emoji: π
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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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π§ **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.*
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