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🔄 In a Training Loop

Dean Byrne PRO

Quazim0t0
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Fishtiks's profile picture sumuks's profile picture JoeMama123123123's profile picture
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https://huggingface.co/DaisyChainAI

AI & ML interests

DaisyChainAI🌼 / SmallLM's / San Francisco / Open Source

Recent Activity

updated a model about 12 hours ago
Quazim0t0/Spikewhale-SNN-Brain2Qwerty
updated a collection about 13 hours ago
My Open-Source: Pretrained Models
posted an update about 15 hours ago
Big update to 🕸️ DaisyChain-Web - the browser demo where your spare devices pretrain a language model together, peer-to-peer. 🌼 Since launch, the demo has grown from a proof-of-concept into something much more real: - 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 (Phones that lock the screen or hop wifi↔cellular reconnect on resume.) - DAISY_RTC_CONFIG - operators can supply their own TURN/ICE config via env var without touching client code. - Split-K f32 backward - Gather-fused attention Net effect of this push: compute step 821ms -> 420ms (1.95×); full 2-device run 177s -> 131s. 🌼 Try the demo: https://huggingface.co/spaces/Quazim0t0/DaisyChain-Web 📦 Full project: https://huggingface.co/DaisyChainAI/DaisyChain-Train _____ ⚡Also: 🌌 Wheeler–DeWitt‑62M - 2B Tokens Pretrain on a 3060 GPU. 📦 Model: https://huggingface.co/Quazim0t0/Wheeler-DeWitt-62M 🧠 Demo Chat: https://huggingface.co/spaces/Quazim0t0/Wheeler-Chat A 62.9M‑parameter research language model whose per‑layer channel mixer is the Wheeler–DeWitt equation of canonical quantum gravity, with a fractal (Cantor‑set) RoPE frequency spectrum. - Elo / Bradley-Terry key rating - keys accumulate a persistent "reputation" score that biases future attention logits, carried through the KV cache. - Channel mixer (the headline): WheelerDeWittBlock = replaces the MLP with a leapfrog integration of the Wheeler-DeWitt wave equation over 64 minisuperspace modes under a Lorentzian DeWitt supermetric (4 wave steps, learnable lapse), with a Hamiltonian-constraint aux loss ⟨H²⟩ pushing each layer toward HΨ=0. - Positional encoding: Fractal RoPE - RoPE frequencies placed on a Cantor set (γ=1.0) instead of the geometric ladder; baked in from scratch.
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