AxionLab-official (AxionLab)

reacted to Quazim0t0's post with 🔥 1 day ago

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657

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 ❤️👀 4 days ago

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10641

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!

12 replies

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replied to their post 6 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 🤯🧠🔥👀🚀😎 6 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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replied to Banaxi-Tech's post 6 days ago

Good!!!!

reacted to their post with ❤️ 7 days ago

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4946

⚠️ Community Notice

We would like to clarify that SupraLabs has no affiliation, partnership, or connection whatsoever with "SupraLarps" or its members.

Please avoid interacting with their organization, repositories, or Spaces under the assumption that they are associated with us.

We are currently aware of the situation and have already contacted the appropriate channels to address it.

Thank you to everyone who continues to support SupraLabs. ❤️

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posted an update 7 days ago

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4946

⚠️ Community Notice

We would like to clarify that SupraLabs has no affiliation, partnership, or connection whatsoever with "SupraLarps" or its members.

Please avoid interacting with their organization, repositories, or Spaces under the assumption that they are associated with us.

We are currently aware of the situation and have already contacted the appropriate channels to address it.

Thank you to everyone who continues to support SupraLabs. ❤️

11 replies

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posted an update 14 days ago

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Please, give a follow to SupraLabs!

We are researching the most, just to make the best medium models FOR YOU!

SupraLabs/Supra-A2A-Nano-Exp

SupraLabs/Supra-1.5-50M-Instruct-exp

SupraLabs/Supra-50M-Reasoning

SupraLabs/supra-title-50M-pre-gguf

Check more at Supralabs org!

SupraLabs

---
@LH-Tech-AI
@QyrouNnet-AI
@LyJonathan
@Mmorgan-ML
@User01110

1 reply

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posted an update 17 days ago

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3407

# An Open Letter from SupraLabs.

Over the past few days, SupraLabs has been mentioned in a public discussion regarding small language models, scaling laws, and training methodology. We'd like to clarify our position.

Before anything else, we want to make one thing absolutely clear: we have great respect for Lane and the work being done at Glint Research. At no point was our intention to disrespect Lane, Glint Research, or their research. What began as a technical discussion about model scaling and training methodology unfortunately became much more personal than we ever intended. From our perspective, it was simply an exchange of technical opinions, and we sincerely hope it remains that way.
We'd also like to acknowledge that one of our own comments during the discussion was poorly worded. Referring to a benchmark as "fake" was imprecise. What we intended to criticize was the comparison methodology, not the integrity of the evaluation itself. Comparing a merged checkpoint against a single checkpoint is, in our view, not an apples-to-apples comparison.

That said, this was never the core of the discussion.

Our disagreement was not about SLERP, model merging, or whether training a small model on massive amounts of data is an interesting research direction. We support experimentation and unconventional ideas.

The actual point of disagreement was much simpler.

The statement that a 1M parameter model trained on 1 trillion tokens will become a "100M killer" is, today, a prediction, not an experimental result.
Could it happen? Perhaps.
Would it be exciting if it did? Absolutely.

But until benchmark results, reproducible evaluations, and independent validation exist, we believe such statements should be presented as hypotheses rather than established conclusions.
Research advances by testing ideas, not by assuming their outcomes.

We sincerely wish Lane and everyone at Glint Research success in their experiments.

Thank you to everyone who read it.

1 reply

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reacted to pedrodev2026's post with 👍 20 days ago

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194

O NanoMathDataset é um dataset de quase 4M linhas de contas de matemática básica, veja ele em: pedrodev2026/NanoMathDataset

replied to their post 29 days ago

It turns out that you can cherry pick results, post an image, and act like it generalizes

wdym lmao, i never said the model generalizes it, i just posted a response about it when i tried it, the model isn't perfect, but isn't cool that a 50M params model quantized in 2Bit answer you like that?

replied to their post 29 days ago

3 days on a single RTX 5060 Ti 16GB

posted an update 30 days ago

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10976

THIS IS CRAZY! THE MODEL ON THE IMAGE(Supra-50M-Reasoning) answered correctly and its QUANTIZED IN 2BIT! THE RESPONSE IS CORRECT, IN A 15MB SIZE FILE!

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posted an update about 1 month ago

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6615

We're happy to announce that we released a Reasoning tuned version of Supra-50M!

SupraLabs/Supra-50M-Reasoning

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