README.md

---
title: DaisyChain-Web
emoji: 🌼
colorFrom: green
colorTo: yellow
sdk: docker
app_port: 7860
pinned: true
license: mit
short_description: Train a shared model P2P by opening a browser tab
---

# 🌼 DaisyChain-Web — train by opening a page

Open this Space on two or more devices and they **pretrain a language model
from scratch** together — peer-to-peer over WebRTC, right in the browser,
computing through verified INT8 neural units (WebGPU, or the same units on CPU
for old machines). This is pretraining, not fine-tuning: every run starts from
random weights.

- **Create a room** and share the invite link with your devices, or **join** a
  room by code — the room's creator approves each device before it can join.
- Pick the training settings with sliders — including any public HuggingFace
  dataset with a `text` column; whoever presses Start sets them for the whole
  group and every device follows automatically.
- Devices that drop are redialed (5 attempts) then removed; devices that join
  mid-run are synced in with a live state transfer; if the sync leader leaves,
  the next peer is promoted and the run continues.
- Gradients are averaged every step with a deterministic Adam optimizer, so all
  devices end with bit-identical weights.
- Download your trained model, or upload a checkpoint to restore the whole group.

Part of **[DaisyChain-Train](https://huggingface.co/DaisyChainAI/DaisyChain-Train)** —
a pipeline for reusing old/spare hardware to train neural networks.

**Heads up:** peers connect directly (WebRTC), so devices in your group can see
each other's IP address, and there is no gradient authentication — only train
with devices/people you trust. Proof of concept.