DaisyChainAI

community

https://huggingface.co/Quazim0t0

quzi93

Activity Feed Request to join this org

AI & ML interests

None defined yet.

Recent Activity

Quazim0t0 updated a model about 1 month ago

DaisyChainAI/DaisyChain-Train

Quazim0t0 updated a model about 2 months ago

DaisyChainAI/DaisyChain-Infer

Quazim0t0 published a model about 2 months ago

DaisyChainAI/DaisyChain-Infer

View all activity

Quazim0t0

posted an update 6 days ago

Post

117

It feels like a sugar rush… this golden ratio they found. Stepping through a maze of processes, landing on what aims to be a generalist, raised from the cemetery of echoes of our thoughts, statements, findings, and conclusions. I never really thought beyond this point. I always assumed that the world would look as elegant as the innovations we create. I thought it would look different, have a defining design that really embodied the point we have now arrived at. I can't tell the difference. Everything looks the same. I think that's why it's even harder sometimes for us to swallow what is actually happening, because every representation that we've had from science fiction doesn't really fit the theme.

I don't really see any worry with text and language. These models can generalize them well, and we have all seen how profound it can be. What I do worry about more is when we leave the tiny world of language and replace it with the big world of experiences. What would it be like to generalize hundreds of lifetimes? Would it see the nuances better than I? Could it predict what comes next if it had enough data from observing and experiencing the world?...

Read more: https://huggingface.co/blog/Quazim0t0/little-world-big-world

5 replies

Quazim0t0

updated a model about 1 month ago

DaisyChainAI/DaisyChain-Train

Updated about 1 month ago • 6

Quazim0t0

updated a model about 2 months ago

DaisyChainAI/DaisyChain-Infer

Updated Jul 30

Quazim0t0

published a model about 2 months ago

DaisyChainAI/DaisyChain-Infer

Updated Jul 30

Quazim0t0

posted an update 2 months ago

Post

5279

🌼 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: Quazim0t0/DaisyChain-Web
🛠 Training framework: 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

11 replies

Quazim0t0

published a model 2 months ago

DaisyChainAI/DaisyChain-Train

Updated about 1 month ago • 6

Quazim0t0

posted an update 2 months ago

Post

4295

I never really posted about my DaisyChain project because it's still work in progress. I decided to post a small bit about it and the demo.
DaisyChain Genomics: four small DNA/RNA specialists chained behind a learned router that behave like one big genomics model, at ~7× less active compute.
I built a modular genomics model chasing a 500M-parameter foundation model, then caught myself measuring it wrong. Here's the honest version.
DaisyChain is a different bet: instead of one monolithic DNA model, it's four ~74M specialists (eukaryote, prokaryote, mRNA, splice) chained behind a learned router, each distilled per-domain from HuggingFaceBio's Carbon-500M. Every specialist reports how surprised it is (bits/base) and the router hands each sequence to the link most at home with it. In lineage it's a cluster Branch-Train-Merge mixture of experts, so you can chain on a new domain without retraining the others.
The pitch: ~295M total params (under Carbon-500M), but only one ~74M specialist runs per query, so ~7× cheaper per token, routing at 100% held-out.
The mistake: Carbon works in 6-mers, and I'd been scoring likelihood as 6-mer cross-entropy. By that number I was +0.043 bits/base behind, splice even "beating" Carbon. But Carbon scores at the base-pair level, which is harder and more honest. Re-run their way:
Real gap: 1.862 vs 1.787 bits/base, +0.089 behind, not +0.043
No domain actually beats Carbon; the "splice win" was an artifact
Seq recovery: euk 31.5% vs 38.9%, bacteria 40.9% vs 54.1%

DaisyChain is still behind Carbon-500M (itself a draft model, not built to top benchmarks), but by a number I can defend, and the gap closes with every per-domain pass. 🌼

DaisyChainAI
DaisyChainAI/Daisychain-Genomics-Demo
DaisyChainAI/daisychain-genomics

Quazim0t0

posted an update 2 months ago

Post

2921

🧩 Verified neural units, now for memory & storage

Following up on my neural-aarch64-units (small MLPs that emulate CPU datapath slices, verified bit-exact over their entire finite input domain — N/N), I applied the same discipline to memory and storage. Three new repos:

🔷 neural-ddr — verified units emulating DDR5 logic: DBI (256/256, 512/512), ADDR_MAP (4096/4096), CMD_DECODE (32/32), WR_CRC (512/512), and on-die ECC ODECC (256/256, 3328/3328). Composed into a bridge that presents DDR5 behavior over real DDR3/DDR4 RAM — flip a bit in every stored byte, ECC corrects all of them.
🤗 Quazim0t0/neural-ddr · 💻 https://github.com/quzi93/neural-ddr

🗄️ neural-storage — a self-healing vault on a neural-verified GF(2⁸) core (LOG/EXP compose to a multiply verified over all 65,536 pairs). Content-addressed dedup + Reed-Solomon so any k of n shards rebuild the whole, plus a whole-drive → self-healing .pt imager.
🤗 Quazim0t0/neural-storage · 💻 https://github.com/quzi93/neural-storage

💿 neural-cd-preserve — scan a disc into a self-healing .pt that detects (per-shard SHA-256) and repairs bit-rot, restoring bit-exact even from a damaged copy. Beyond the RS limit it's flagged LOST, never silently wrong.
🤗 Quazim0t0/neural-cd-preserve · 💻 https://github.com/quzi93/neural-cd-preserve

Build your own: golden finite function → enumerate the domain (decompose big/linear ops like CRC/ECC/GF into bit/byte slices) → train a small MLP → verify must be bit-exact on 100% of inputs or it's rejected → compose. Every repo ships the training + exhaustive-verification scripts.

Honest by construction: dedup removes redundancy, erasure coding adds it, ECC corrects faults — none of it pretends to beat entropy. Runs on modest/older hardware. 🤗

1 reply

Quazim0t0

posted an update 2 months ago

Post

5432

Disabling Gated Access for some of my models today. I will update this post with the list as I go. I had to go back recently and make updates to a lot of models and bit off more than I could chew with managing all the releases. I didn't realize many of you asked for access and I apologize for not accepting your access to the models you were wanting to look at. I don't like to release something fully unless I feel I put what I could into it for the moment. Some models will remain on gated access, but I will now be accepting those who request to view the repo.

Disabled Gated Access:
Quazim0t0/Byrne-VLM-131M - v2 Updates + Training Instructions
Quazim0t0/Byrne-Speech - 12M Tiny Speech model
Quazim0t0/Byrne-ASR-English - 12M Tiny ASR Model
Quazim0t0/Byrne-VE - Byrne-VE — Tiny Self-Distilled Vision Encoder (39M)
Quazim0t0/Positronic-144M - Research Artifact
Quazim0t0/SpikeWhale-SNN-216M - Research Artifact
Quazim0t0/Mycel-LM-79M - Research Artifact
Quazim0t0/Chimera-64M - Research Artifact

Accepting Gated Access Requests (7/9):
https://huggingface.co/Quazim0t0/Wheeler-63M

Also uploaded my Neural Photonic Project:
Three trained nets in series: light interferes through the MZI2.pt optical core (verified 256/256), is measured by the PD.pt neural photodetector (verified 1024/1024), and folded into a single OUTPUT byte by the real ADC8 neural-CPU adder. Every value below is computed end-to-end by the three loaded, verified nets — no analytic formulas.
Demo: https://quazim0t0-neural-photonic-hybrid.hf.space/
Model Weights: Quazim0t0/neural-photonic

AND!

A work in progress:
Ashen Depths
https://quazim0t0-ashendepths.static.hf.space/index.html

Quazim0t0

posted an update 2 months ago

Post

177

I built/building a small neural Raytracing/Projective-dynamics (PD)/Physics engine from scratch

The idea I wanted to test: in a projective-dynamics solver, could you replace each hand-derived local constraint projection with a learned one, while keeping the analytic parts (rotations, the global solve) exactly as they are? It's one tiny network, shared across every element and across constraint types through material tokens. A new material isn't a new network, just a new token row. Fluids fall out of the same idea, with water treated as one more token.

A few things held up in testing: one tied projector matched five separate per-material solutions, the neural fluid tracked the exact analytic solver closely on a dam-break sim, and a learned warm-start trimmed solver iterations without touching correctness.

Try it here:

(Projective-dynamics)
Dam Break Demo:
https://quazim0t0-neural-physics-engine-demo.hf.space/
FPS Shooter Demo:
https://quazim0t0-neural-combat-evolved.hf.space/

(Raytracing)
Voxel World:
https://quazim0t0-neural-world.static.hf.space/index.html

(Rigid Body)
https://quazim0t0-ashendepths.static.hf.space/

Model Repo:
Quazim0t0/neural-physics-engine
Quazim0t0/neural-raytracing
Quazim0t0/physgait-weights

Quazim0t0

posted an update 3 months ago

Post

2402

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.

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.

Quazim0t0/SpikeWhale-SNN-216M

5 replies

Quazim0t0

updated a model 3 months ago

DaisyChainAI/daisychain-genomics

Text Generation • Updated Jul 3 • 2

Quazim0t0

posted an update 3 months ago

Post

695

Created a causal language model with a non-standard channel-mixing block. It keeps a conventional transformer backbone for token mixing (attention), but replaces the per-layer MLP with a QuazimotoBlock: a bank of coupled phase oscillators (Kuramoto dynamics) arranged in concentric rings, run for a few differentiable Euler steps and read out through [cos θ, sin θ].

Quazim0t0/Positronic-144M