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AI & ML interests
Computational semiotics is empirically proven. It takes three to tango ๐๐ชฉ๐บ
Recent Activity
reacted to Quazim0t0's post with ๐ฅ about 12 hours ago ๐ผ DaisyChain-Web: train a language model with friends or by yourself with multiple devices, in the browser, no install
Open a webpage, share a room link, and every device that joins becomes part of the training cluster. Phones, laptops, old PCs: they connect peer-to-peer over WebRTC and train one shared transformer together, entirely in the browser.
What's actually happening under the hood:
๐ง A mini transformer LM trains on FineWeb-Edu, streamed live from the HuggingFace Hub. Each device pulls its own slice (data parallelism), tokenized with our 16.5k-token Spikewhale tokenizer
โก Every single multiply runs through verified INT8 neural units, no float fallback. On WebGPU browsers it uses the GPU's DP4A integer dot-product hardware, admitted only after proving bit-identical results against the verified units, with a 3รINT8 fast-accurate scheme (CUTLASS's 3xTF32 trick, ported to 8-bit)
๐ Devices average gradients every step under a sync guard: a per-step roster protocol plus weight-hash verification keeps every device's model bit-identical. If anything drifts, training stops instead of silently forking
๐ Live logs show exactly what every device contributes, step by step
๐พ When you're done: test generations right on the page, download a checkpoint, or grab the inference kit, a single self-contained HTML file with the weights baked in that runs generations offline, anywhere
Works solo too. Every extra device just grows the effective batch.
๐ Try it: https://huggingface.co/spaces/Quazim0t0/DaisyChain-Web
๐ Training framework: https://huggingface.co/DaisyChainAI/DaisyChain-Train
Proof of concept: only train with devices you trust. Feedback welcome! Organizations
