| # 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 |
|
|
| ```bash |
| 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) |
|
|
| ```bash |
| 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](../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](VERIFICATION.md).) |
| - This is a proof of concept, not a hardened public service. |
|
|
| ## More |
|
|
| - [ARCHITECTURE.md](ARCHITECTURE.md) — how the mesh, the training step, and |
| the wire protocol work. |
| - [VERIFICATION.md](VERIFICATION.md) — why you can trust the numbers. |
| - [TROUBLESHOOTING.md](TROUBLESHOOTING.md) — common failure modes and what |
| the log messages mean. |
|
|
