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π§ββοΈ
hugging magician
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enzo
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enzostvs
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enzostvs
enzostvs
enzostvs
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atalaydenknalbant/Jump-Rope-5.6-Sol
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πΌ 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: π§ 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 β‘ 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 3ΓINT8 fast-accurate scheme (CUTLASS's 3xTF32 trick, ported to 8-bit) π 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 π Live logs show exactly what every device contributes, step by step πΎ 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. π Try it: https://huggingface.co/spaces/Quazim0t0/DaisyChain-Web π Training framework: https://huggingface.co/DaisyChainAI/DaisyChain-Train Proof of concept: only train with devices you trust. Feedback welcome!
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enzostvs
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enzostvs/robert-traces
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5 days ago
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enzostvs/test-default-avatar
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May 29
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enzostvs/deepsite-draw-to-code
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Feb 6
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enzostvs/figma-push-to-dataset
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Sep 23, 2024
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enzostvs/stable-diffusion-tpu-generations
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Feb 22, 2024
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enzostvs/what-is-the-prompt
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Feb 20, 2024
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