web/docs/GETTING_STARTED.md

Getting started with DaisyChain-Web

DaisyChain-Web trains a small transformer language model peer-to-peer in the browser. Every device that opens the page becomes a training node: it computes through the verified INT8 units (WebGPU, or the identical math on CPU), exchanges gradients with the other devices over WebRTC, and all replicas stay bit-identical — same weights, same loss, on every device, every step.

The fastest way: the hosted Space

Open https://huggingface.co/spaces/Quazim0t0/DaisyChain-Web on two or more devices. That's it — no install. Devices on the same network find each other automatically (Snapdrop-style, grouped by public IP). To train with devices on different networks, use a room code (below).

Run it yourself

npm install
npm start            # serves on http://localhost:8787

Open http://localhost:8787 in two browser tabs to try it on one machine.

HTTPS matters. WebGPU and cross-device WebRTC require a secure context: localhost or HTTPS. Plain http://192.168.x.x from another device will NOT get WebGPU (and may not connect at all). For real multi-device runs, serve over HTTPS — a tunnel (cloudflared, ngrok), a host, or a Hugging Face Space.

Rooms: training across networks

Everyone opens the same URL with a shared code:

https://<host>/?room=MY-SECRET-CODE

The first person in the room is the host and must approve each device before it can join (an Accept button appears per request). The room code is never guessable from the public page; pick something private.

Starting a run

1. Wait until the devices appear in each other's peer list.

2. On one device, pick the settings (that device's choices apply to the whole group):

   Setting	Range	Default	What it does
   Model width	16–128	32	embedding/channel width of the transformer
   Sequence length	16–128	32	context window in tokens
   Batch per device	2–32	8	each device adds this much batch — more devices = bigger effective batch
   Steps	100–10000	300	training steps for the run
   Learning rate ×1000	5–50	10	Adam learning rate (10 = 0.01)

3. Press Start training. Every device begins the same run: the starter broadcasts the config, everyone builds the same seeded weights, and the loss falls in lockstep on all of them.

The replica diff / weight hash readout is the trust signal: every honest device shows the same hash at the same step. Training data streams from FineWeb-Edu (served by the Space from the HF CDN's parquet shards); devices that can't reach it fall back to a built-in corpus.

Devices that join mid-run are synced in: the leader ships them the current weights and step, and they participate from the next step.

After training: generate, save, share

• Generate — type a prompt and sample from the trained model, right on the page.
• Download checkpoint (.pt) — saves weights + step + config. Load it later on any device; the loader validates the tokenizer and dimensions, and loading on one device broadcasts the checkpoint to the whole group, so one person can restore everyone after a failure.
• Download inference kit — a single self-contained HTML file with the weights baked in. Open it anywhere, even offline, to run generations. Handy for sharing what the group trained.

Checking the math (no browser needed)

npm test

runs all ten verification suites — convergence, bit-identical replicas, the IEEE-754 oracle, kernel gates, metamorphic properties, the external bug corpus, the B2B chain, optimizer, transformer, and the int8 backward. Results with methodology live in TEST_RESULTS.md.

Safety notes

• WebRTC is direct: devices in a group can see each other's IP addresses.
• There is no gradient authentication — a malicious peer could poison the model. Train with devices and people you trust. (Dishonest math is a different story: a device whose kernel computes wrong values is caught by the kernel probe and audits — see VERIFICATION.md.)
• This is a proof of concept, not a hardened public service.

More

• ARCHITECTURE.md — how the mesh, the training step, and the wire protocol work.
• VERIFICATION.md — why you can trust the numbers.
• TROUBLESHOOTING.md — common failure modes and what the log messages mean.