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reacted to Quazim0t0's post with 🤯 5 days ago
🌼 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: 🧠 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 ⚡ 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 3×INT8 fast-accurate scheme (CUTLASS's 3xTF32 trick, ported to 8-bit) 🔒 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 📊 Live logs show exactly what every device contributes, step by step 💾 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. 👉 Try it: https://huggingface.co/spaces/Quazim0t0/DaisyChain-Web 🛠 Training framework: https://huggingface.co/DaisyChainAI/DaisyChain-Train Updates: - Block-scaled INT8 quantization - Batched attention GEMM - Fused dequant+ReLU epilogue - Weight-tied unembedding - WebSocket relay fallback - Server keepalive ping/pong every 30s - disconnected-state redial - Visibility/network-change reconnect
reacted to Quazim0t0's post with 🔥 5 days ago
🌼 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: 🧠 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 ⚡ 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 3×INT8 fast-accurate scheme (CUTLASS's 3xTF32 trick, ported to 8-bit) 🔒 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 📊 Live logs show exactly what every device contributes, step by step 💾 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. 👉 Try it: https://huggingface.co/spaces/Quazim0t0/DaisyChain-Web 🛠 Training framework: https://huggingface.co/DaisyChainAI/DaisyChain-Train Updates: - Block-scaled INT8 quantization - Batched attention GEMM - Fused dequant+ReLU epilogue - Weight-tied unembedding - WebSocket relay fallback - Server keepalive ping/pong every 30s - disconnected-state redial - Visibility/network-change reconnect
reacted to Quazim0t0's post with 🚀 5 days ago
🌼 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: 🧠 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 ⚡ 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 3×INT8 fast-accurate scheme (CUTLASS's 3xTF32 trick, ported to 8-bit) 🔒 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 📊 Live logs show exactly what every device contributes, step by step 💾 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. 👉 Try it: https://huggingface.co/spaces/Quazim0t0/DaisyChain-Web 🛠 Training framework: https://huggingface.co/DaisyChainAI/DaisyChain-Train Updates: - Block-scaled INT8 quantization - Batched attention GEMM - Fused dequant+ReLU epilogue - Weight-tied unembedding - WebSocket relay fallback - Server keepalive ping/pong every 30s - disconnected-state redial - Visibility/network-change reconnect
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