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PhysiQuanty
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AI & ML interests

Theoretical Physics, Invariant Tokenization, Standard Model of Particle Physics Applied ML 🇫🇷

Recent Activity

liked a model about 2 hours ago
WhirlwindAI/Qwen-R1-0.5B
liked a model about 6 hours ago
Quazim0t0/Mycel-LM-79M
reacted to Quazim0t0's post with 🔥 about 6 hours ago
Created research language model whose channel-mixing block is not an MLP. It is a differentiable Neighbour-Sensing fungal-colony-growth model: each token is expanded into a colony of hyphal tips that grow in a bounded latent region, sense a shared density field, and steer their own growth — the "MLP" is replaced by a few differentiable steps of colony growth, read back out into the hidden state. https://huggingface.co/Quazim0t0/Mycel-LM-79M Also the original SpikeWhale project — the one that sparked all the other SpikeWhale related projects. Every spiking primitive here is hand-written in plain PyTorch: the leaky integrate-and-fire (LIF) neuron dynamics, the fast-sigmoid surrogate gradient, and the backprop-through-time training loop. No snntorch, no spikingjelly, no norse, no bindsnet — the network is a genuine from-scratch SNN. https://huggingface.co/Quazim0t0/SpikeWhale-SNN-216M
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