Yea i saw it too, they're spamming gore, porn, nazi and racist content in supralabs repos discussions
🔄 In a Training Loop
AxionLab
AxionLab-official
AI & ML interests
Owner of SupraLabs and iGPU Lover
Recent Activity
new activity about 4 hours ago
Tralalabs/TralaBPE-32K-EnglishMix-v1:STOP! new activity about 19 hours ago
SupraLarps/dumblabs-1234b:Yeah liked a model 2 days ago
BananaMind/MiniBananaMind-v3-9MOrganizations
replied to their post 2 days ago
reacted to SeaWolf-AI's post with 🤯🧠🔥👀🚀😎 2 days ago
Post
4916
🐯 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
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
- 1 reply
replied to Banaxi-Tech's post 2 days ago
Good!!!!
reacted to their post with ❤️ 3 days ago
Post
4884
⚠️ 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. ❤️
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
posted an update 3 days ago
Post
4884
⚠️ 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. ❤️
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
posted an update 10 days ago
Post
8392
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
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!
---
@LH-Tech-AI
@QyrouNnet-AI
@LyJonathan
@Mmorgan-ML
@User01110
- 1 reply
posted an update 13 days ago
Post
3403
# 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.
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
reacted to pedrodev2026's post with 👍 16 days ago
Post
194
O NanoMathDataset é um dataset de quase 4M linhas de contas de matemática básica, veja ele em: pedrodev2026/NanoMathDataset
replied to their post 25 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 25 days ago
3 days on a single RTX 5060 Ti 16GB
posted an update 26 days ago
Post
10967
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!
- 14 replies
posted an update 28 days ago
Post
6611
We're happy to announce that we released a Reasoning tuned version of Supra-50M!
SupraLabs/Supra-50M-Reasoning
SupraLabs/Supra-50M-Reasoning
reacted to their post with 🚀 about 1 month ago
Post
270
Someone ran Supra-50M-Instruct ON A 1GHZ 1999 CPU
https://www.reddit.com/r/LocalLLM/comments/1tm21ar/i_see_your_strix_halo_and_raise_you_a_vintage/
"As a fun experiment, I decided to try running the recently released Supra-50m on a 26-year-old machine I keep for retro Windows 9.X games. Although the model was somewhat silly and inconsistent, the performance wasn't bad, reaching around 1.3 tok/s with CPU inference alone.
Since this CPU doesn't have SSE2, I changed from llama.cpp to llama2.ce and asked Claude to write a custom tokenizer.
It's crazy to think that with the right file size of 200 MB, we could have experienced this magic back in 1999" - u/drone_stonks, r/localllm
https://www.reddit.com/r/LocalLLM/comments/1tm21ar/i_see_your_strix_halo_and_raise_you_a_vintage/
"As a fun experiment, I decided to try running the recently released Supra-50m on a 26-year-old machine I keep for retro Windows 9.X games. Although the model was somewhat silly and inconsistent, the performance wasn't bad, reaching around 1.3 tok/s with CPU inference alone.
Since this CPU doesn't have SSE2, I changed from llama.cpp to llama2.ce and asked Claude to write a custom tokenizer.
It's crazy to think that with the right file size of 200 MB, we could have experienced this magic back in 1999" - u/drone_stonks, r/localllm
posted an update about 1 month ago
Post
270
Someone ran Supra-50M-Instruct ON A 1GHZ 1999 CPU
https://www.reddit.com/r/LocalLLM/comments/1tm21ar/i_see_your_strix_halo_and_raise_you_a_vintage/
"As a fun experiment, I decided to try running the recently released Supra-50m on a 26-year-old machine I keep for retro Windows 9.X games. Although the model was somewhat silly and inconsistent, the performance wasn't bad, reaching around 1.3 tok/s with CPU inference alone.
Since this CPU doesn't have SSE2, I changed from llama.cpp to llama2.ce and asked Claude to write a custom tokenizer.
It's crazy to think that with the right file size of 200 MB, we could have experienced this magic back in 1999" - u/drone_stonks, r/localllm
https://www.reddit.com/r/LocalLLM/comments/1tm21ar/i_see_your_strix_halo_and_raise_you_a_vintage/
"As a fun experiment, I decided to try running the recently released Supra-50m on a 26-year-old machine I keep for retro Windows 9.X games. Although the model was somewhat silly and inconsistent, the performance wasn't bad, reaching around 1.3 tok/s with CPU inference alone.
Since this CPU doesn't have SSE2, I changed from llama.cpp to llama2.ce and asked Claude to write a custom tokenizer.
It's crazy to think that with the right file size of 200 MB, we could have experienced this magic back in 1999" - u/drone_stonks, r/localllm