AxionLab-official (AxionLab)

reacted to appvoid's post with 🚀🔥 3 days ago

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4526

if you are a tinkerer of small language models and want to stay ahead of what small models can do, follow me!!! seriously, start following people that actually still makes small models

i've made one recently btw

also, i'm keeping an eye on AxiomicLabs leaderboard, looks like the only current alternative to check where the things are going to

though, between us, i think they should add agentic/tool use benchmarks there

anyways,

enjoy!

appvoid/a-cool-model

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reacted to Banaxi-Tech's post with 🧠😎🤯👀🚀🔥➕ 3 days ago

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2508

Introducing BananaMind 2 Nano

BananaMind 2 Nano is the smallest member of the BananaMind 2.0 family — a 10M-parameter language model that shows how much you can squeeze out of a tiny footprint. It uses the family's digit-isolated tokenizer, so it keeps solid arithmetic despite its size, and it's small enough to run just about anywhere.

Trained on 30B tokens in about a day on a single RTX 5070 Ti (16GB), 4096-token context.

Benchmarks:

Average 35.77
ARC Easy 36.20
PIQA 55.98
ARC Challenge 23.38
HellaSwag 27.50

That 35.77 average edges out Pythia-31M (~34.79) at roughly a third the parameters.

Released under Apache 2.0 on Hugging Face: BananaMind/BananaMind-2-Nano — weights, tokenizer, and config included.

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reacted to wop's post with 🤗 3 days ago

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3604

# PixelModel v1

Last month we released PixelModel — a neural network whose weights are literally the pixels of a PNG. It was a toy: 202,752 parameters, welded to 32×32 output, trained on six solid-color swatches. It scored FID 566.84 on the Tiny-T2I-Leaderboard, mostly by producing the same yellow noise for every prompt.

Today we're releasing PixelModel v1. It is 8.5× smaller — 23,747 parameters — and it beats v0 on both benchmark metrics while being trained on 20,000 real MS-COCO caption/image pairs instead of six color swatches. The entire model now fits in a 160×149 PNG.

That image is not a visualization of the model. It is the model. All 23,747 weights, one per pixel.

## links

bench-labs
Blog post [read it here ⇗]( bench-labs/blog)
See us on the [Leaderboard ⇗]( FlameF0X/Tiny-T2I-Leaderboard)
Model card [here]( bench-labs/pixelmodel-v1)

## The catch
A 23K-parameter model does not draw sandwiches. With ~1 parameter per training image, the loss-minimizing behavior is to output the average of all plausible images for a caption — caption-conditioned color, light, and layout statistics. Food prompts come out warm and brown; sky prompts come out cool and bright. That is the ceiling for this size class, and we'd rather show it than crop around it.

# cherry on top 🍒
The model generates 600 images (cpu) in 5 (five) seconds.
Thats 5000 images in 24 seconds on cpu.
The model trained on cpu for just 30 minutes.

reacted to wop's post with 👀🔥 4 days ago

Post

3604

# PixelModel v1

Last month we released PixelModel — a neural network whose weights are literally the pixels of a PNG. It was a toy: 202,752 parameters, welded to 32×32 output, trained on six solid-color swatches. It scored FID 566.84 on the Tiny-T2I-Leaderboard, mostly by producing the same yellow noise for every prompt.

Today we're releasing PixelModel v1. It is 8.5× smaller — 23,747 parameters — and it beats v0 on both benchmark metrics while being trained on 20,000 real MS-COCO caption/image pairs instead of six color swatches. The entire model now fits in a 160×149 PNG.

That image is not a visualization of the model. It is the model. All 23,747 weights, one per pixel.

## links

bench-labs
Blog post [read it here ⇗]( bench-labs/blog)
See us on the [Leaderboard ⇗]( FlameF0X/Tiny-T2I-Leaderboard)
Model card [here]( bench-labs/pixelmodel-v1)

## The catch
A 23K-parameter model does not draw sandwiches. With ~1 parameter per training image, the loss-minimizing behavior is to output the average of all plausible images for a caption — caption-conditioned color, light, and layout statistics. Food prompts come out warm and brown; sky prompts come out cool and bright. That is the ceiling for this size class, and we'd rather show it than crop around it.

# cherry on top 🍒
The model generates 600 images (cpu) in 5 (five) seconds.
Thats 5000 images in 24 seconds on cpu.
The model trained on cpu for just 30 minutes.

reacted to Banaxi-Tech's post with 🤯👍 7 days ago

Post

3789

We are announcing 3 more models in our BananaMind 2 Family of models!
BananaMind 2 Nano, a small 10M parameter model, fits on your Pentium 4
BananaMind 2 Medium, our medium model, 50M parameters
BananaMind 2 MoE, 25M parameters, 2M active per tokens as fast as a 2M.

Because of this our release dates have changed a bit our currently estimates are:
BananaMind 2 MoE July 16-18
BananaMind 2 Nano July 18-20
BananaMind 2 Medium July 24-28
BananaMind 2 Pro August 10-16
Keep in mind these dates are estimates and we don't have a speed number currently, we will post for details going forward!

reacted to Quazim0t0's post with 🔥 17 days ago

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694

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

reacted to Banaxi-Tech's post with ❤️👀 20 days ago

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10752

A new model is coming!
Its going to take a long time on my 5070 Ti so expect a release in ~1 month.
We think this model is going to be SOTA For its size.
Our Mini Version will be 25M Parameters and Pro with 140M.
The Pro version has a 3072 Context Window (Extensible to up to 6K with RoPE) And the Mini version has a context window of 4096 (Up to 8K with RoPE)
Meanwhile we are currently working on a Instruct Version of our BananaMind 1.5 Base.

The training will start this weekend

We are very exited to release it when its done!

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replied to their post 22 days ago

Yea i saw it too, they're spamming gore, porn, nazi and racist content in supralabs repos discussions

reacted to SeaWolf-AI's post with 🤯🧠 22 days ago

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🐯 Chitos — The Security Scanner That Actually Proves It

Most security scanners hand you a suspect list and walk away. That gap between detection and proof is where attackers live — and it's exactly the gap that Chitos was built to close.

Chitos is the successor to Mythos, a static analyzer built for quick code health checks. Mythos was good at pattern matching — spotting dangerous sinks, mapping CWEs, producing readable reports. But static analysis has a structural ceiling. A rule that sees eval(user_input) can tell you that looks dangerous. It cannot tell you whether the input is reachable, whether sanitization three layers up covers this path, or whether there's a live exploit chain for your exact framework version. Chitos was built to answer those questions.

🔍 Phase 1 applies 50 language-agnostic rules across Python, JavaScript, Go, Java, C/C++, Rust, PHP, YAML and more — covering injection sinks, deserialization gadgets, credential leakage, broken crypto, and prototype pollution. Every candidate is re-verified before reaching the report. Findings that can't be substantiated are excluded, not handed to you as noise.

🔬 Phase 2 dispatches an autonomous web-search agent to hunt live CVE databases, exploit advisories, and public PoC repositories. It formulates hypotheses, verifies them, and synthesizes a structured threat narrative. This phase needs a user-supplied Claude API key — Phases 1 and 3 run entirely free.

🎯 Phase 3 is where Chitos diverges from everything else. Against targets you own or are authorized to test, it fires real payloads — XSS, SQLi, path traversal, command injection — mutates on block, captures hard evidence, and connects every proven finding into a kill-chain showing which vulnerabilities to remediate first.

No installation. No account. No code sent to third-party APIs.

Article: https://huggingface.co/blog/FINAL-Bench/chitos

Try it now 👉 https://chitos.vidraft.net

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