Quazim0t0/Spikewhale-SNN-Brain2Qwerty
Text Generation • Updated
Dean Byrne PRO
Quazim0t0
AI & ML interests
DaisyChainAI🌼 / SmallLM's / San Francisco / Open Source
Recent Activity
updated a model about 5 hours ago
Quazim0t0/Spikewhale-SNN-Brain2Qwerty updated a collection about 6 hours ago
My Open-Source: Pretrained Models posted an update about 8 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.Organizations
updated a
model about 5 hours ago
updated a
collection about 6 hours ago
posted an update about 8 hours ago
Post
55
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: Quazim0t0/DaisyChain-Web
📦 Full project: DaisyChainAI/DaisyChain-Train
_____
⚡Also:
🌌 Wheeler–DeWitt‑62M - 2B Tokens Pretrain on a 3060 GPU.
📦 Model: 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.
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: Quazim0t0/DaisyChain-Web
📦 Full project: DaisyChainAI/DaisyChain-Train
_____
⚡Also:
🌌 Wheeler–DeWitt‑62M - 2B Tokens Pretrain on a 3060 GPU.
📦 Model: 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.
updated a
model about 9 hours ago
published a
model about 9 hours ago
reacted to dronefreak's post with 🔥 about 9 hours ago
Post
2126
🚀 Excited to open-source the **UAVid Semantic Segmentation Model Zoo** on Hugging Face.
This release includes:
* 📦 A **YOLO-compatible mirror** of the UAVid semantic segmentation dataset, preserving the original train/val/test splits while reorganizing the directory structure for plug-and-play use with modern training pipelines.
* 🤖 Multiple **YOLO26 semantic segmentation models** trained on UAVid, spanning Nano through Medium variants.
* 📊 Detailed model cards with evaluation metrics, per-class IoU, confusion matrices, qualitative results, and training configurations for reproducibility.
The goal is to make benchmarking and experimenting with aerial semantic segmentation easier by providing ready-to-use datasets and pretrained models in a consistent format.
If you're working on UAV perception, autonomous drones, robotics, remote sensing, or real-time semantic segmentation, I hope these resources are useful.
**📦 Dataset:** dronefreak/UAVid-2020
**🤖 Model Collection:** https://huggingface.co/collections/dronefreak/uavid-semantic-segmentation-model-zoo
Feedback, bug reports, and contributions are always welcome.
This release includes:
* 📦 A **YOLO-compatible mirror** of the UAVid semantic segmentation dataset, preserving the original train/val/test splits while reorganizing the directory structure for plug-and-play use with modern training pipelines.
* 🤖 Multiple **YOLO26 semantic segmentation models** trained on UAVid, spanning Nano through Medium variants.
* 📊 Detailed model cards with evaluation metrics, per-class IoU, confusion matrices, qualitative results, and training configurations for reproducibility.
The goal is to make benchmarking and experimenting with aerial semantic segmentation easier by providing ready-to-use datasets and pretrained models in a consistent format.
If you're working on UAV perception, autonomous drones, robotics, remote sensing, or real-time semantic segmentation, I hope these resources are useful.
**📦 Dataset:** dronefreak/UAVid-2020
**🤖 Model Collection:** https://huggingface.co/collections/dronefreak/uavid-semantic-segmentation-model-zoo
Feedback, bug reports, and contributions are always welcome.
updated a
collection about 10 hours ago
liked a
model about 10 hours ago
updated a
collection about 12 hours ago
updated a
model about 13 hours ago
updated a
Space about 13 hours ago
reacted to AbstractPhil's post with 🔥 about 16 hours ago
Post
2136
Massive AlephLM success. The task collective is producing powerful MOE shared knowledge adapters. A serious success and a massive first step towards the next stage. The current family collective results are present here; AbstractPhil/geolip-aleph-qwen
This is akin to a stackable non-intrusive lora that enables increased shared collective behavior.
This includes the three mentioned json tasks, a math task, a tinystories task, and a diffusion task for cifar10. Each adapter anchored to the knowledge within model that already exists while enhancing the knowledge through anchored lookup systems and decision-driven hierarchical access trees.
All tasks activate independently upon manual override, all tasks handle direct shared knowledge when left to greedy decoding, each task issued multiple tests alongside to determine fidelity and accuracy throughout the process.
The results show the gating is more than willing to hop from sector to sector, using alternating weight shifts from the cooperative anchored systems - even systems never trained for the tasks contributing to the accuracy of the results for other tasks due to the lookup accuracy to the heuristic chains, never having seen the tasks before. Each structure is independently trained and the collective cooperates together through a dense activation network.
Full writeup and article https://huggingface.co/blog/AbstractPhil/aleph-autoregression-differentiation-ft2.
This is akin to a stackable non-intrusive lora that enables increased shared collective behavior.
This includes the three mentioned json tasks, a math task, a tinystories task, and a diffusion task for cifar10. Each adapter anchored to the knowledge within model that already exists while enhancing the knowledge through anchored lookup systems and decision-driven hierarchical access trees.
All tasks activate independently upon manual override, all tasks handle direct shared knowledge when left to greedy decoding, each task issued multiple tests alongside to determine fidelity and accuracy throughout the process.
The results show the gating is more than willing to hop from sector to sector, using alternating weight shifts from the cooperative anchored systems - even systems never trained for the tasks contributing to the accuracy of the results for other tasks due to the lookup accuracy to the heuristic chains, never having seen the tasks before. Each structure is independently trained and the collective cooperates together through a dense activation network.
Full writeup and article https://huggingface.co/blog/AbstractPhil/aleph-autoregression-differentiation-ft2.
reacted to ProCreations's post with 👍 about 16 hours ago
Post
2020
who want grug 35b?
- 5 replies
liked a
model about 16 hours ago
liked 2
models about 18 hours ago
updated a
Space about 18 hours ago