Desperados for Human Singularity

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fuzzy-mittenz updated a bucket 6 days ago

BlackCubeBanditos/NVIDIA-Nemotron-3.5-Bolt-E4B-Uncensored-GGUF-bucket

fuzzy-mittenz published a bucket 6 days ago

BlackCubeBanditos/NVIDIA-Nemotron-3.5-Bolt-E4B-Uncensored-GGUF-bucket

fuzzy-mittenz updated a bucket 7 days ago

BlackCubeBanditos/kilo_super_agent_data-bucket

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takarajordan

posted an update about 17 hours ago

Post

Super impressed with the UltraBERT team at @dmis-lab — @affjljoo3581 , @Taewhoo and Jaewoo Kang — for continuing to push encoders forward. Really impressive work!

I knocked up an MoE version on a single A100, entirely for fun: UltraBERT-Embed-MoE-8x.

8 experts per routed layer, 2 active per token. Works pretty well in my small retrieval tests!

Huge credit to the original team for making this possible.

Original: dmis-lab/UltraBERT-Embed-150M
MoE: takarajordan/UltraBERT-Embed-MoE-8x

Parveshiiii

posted an update 1 day ago

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1,756

Most AI image detectors work fine in the lab and fail on social media.

The reason is simple. Real photos get compressed, resized and re-uploaded again and again, and detectors trained on clean images read that damage as a sign of fakery. A real photo that has passed through a few platforms often gets flagged as AI-generated.

AIRealNet-2 is built to fix that. It is the upgrade to our original Modotte/AIRealNet, trained on images collected from platforms like Instagram and X, so it has seen the kinds of quality loss and compression real images go through online.

The training set has 98k images, split 50/50 between AI-generated and real, so the model has no class bias to lean on. Personal photos were excluded by design, including AI-edited ones.

The architecture is unchanged: a SwinV2 Tiny backbone with a binary classification head, about 0.2B parameters, working at 256x256 resolution. It stays fast and easy to deploy for research or production.

Model: Modotte/AIRealNet-2

3 replies

SeaWolf-AI

posted an update 3 days ago

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5,361

HF Post draft (English) — FreeWeb Security SCAN
🛡 FreeWeb Security SCAN — free web security & AI-visibility check

Drop in a domain and get a non-intrusive (read-only) assessment in ~10–25s. No login, no install, no credit card.

What it checks Security headers · HTTPS/HSTS · cookies · TLS certificate · CORS · exposed files; email security (SPF · DKIM · DMARC · BIMI · MTA-STS · TLS-RPT); DNS (DNSSEC · CAA · MX · NS); site-wide mixed content & external-script SRI; SEO foundations; and reachability of 9 AI/search crawlers (GPTBot · ClaudeBot · PerplexityBot · OAI-SearchBot · Googlebot…) — your AI visibility (GEO).

Why now AI agents now break in on their own. In 2026 the open-source AI penetration agent ARTEX was turned on real targets — hitting 2,250+ Microsoft 365 tenants, breaching e-commerce order databases, and automating recon-to-exfiltration against unpatched internet-facing systems worldwide. Attacks are automated — so must defense be.

3 steps ① Free instant scan → ② Domain-verified deep scan (HF login on your org-domain email) → ③ In-depth consultation (Web · AX Infra · AI Model · AI Agent). Aligned to OWASP · NIST · ISO · EU AI Act.

👉 Scan now: FINAL-Bench/FreeWeb-Security-SCAN

#websecurity #cybersecurity #AIsecurity #SEO #GEO #AIvisibility #SPF #DMARC #OWASP #LLM

1 reply

SeaWolf-AI

posted an update 4 days ago

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7,191

We pretrained a 325M language model from zero on an inference-only NPU

The accepted view is that inference-only NPUs cannot train, let alone pretrain a language model from scratch. We put that view to a direct test on the Rebellions ATOM-Max, a commercially available Korean NPU released in 2025 and designed for inference. We built our own training loop that runs backpropagation on the chip itself. Starting from random initialization, we pretrained VKORE-325M-NPU-Base, a 325.6M parameter model, on 5 billion byte tokens of FineWeb-Edu. The run took 76,292 steps over about four days and finished without a single divergence. No pretrained weights were used.

To make the comparison fair, we trained the same model on a B200 GPU with identical initialization, data and learning-rate schedule. On 1,024 windows of text that neither model ever saw, the NPU reached a loss of 0.7993 and the GPU 0.7994. The window-by-window difference is -0.0001, well inside the noise. The two models are indistinguishable.

This is seven times larger than our first NPU model, VKORE-46M, and the first time this hardware has carried a real pretraining run to GPU quality. Please note that the hardware alone does not make this possible: the NPU and its standard software are built for inference, and training on it depends on our own training stack and stability technique, which are developed in house and under patent application. Buying the chip does not by itself enable training. Both sets of weights and the evaluation script are public, so anyone can verify the result in minutes.

📄 Technical report: https://huggingface.co/blog/FINAL-Bench/pretraining-a-325m-language-model-from-zero-on-an
🤗 Models: FINAL-Bench/VKORE-325M-NPU-Base

#NPU #Rebellions #Pretraining #VKORE

8 replies

fuzzy-mittenz

updated a bucket 6 days ago

BlackCubeBanditos/NVIDIA-Nemotron-3.5-Bolt-E4B-Uncensored-GGUF-bucket

12.4 GB

fuzzy-mittenz

published a bucket 6 days ago

BlackCubeBanditos/NVIDIA-Nemotron-3.5-Bolt-E4B-Uncensored-GGUF-bucket

12.4 GB

fuzzy-mittenz

updated a bucket 7 days ago

BlackCubeBanditos/kilo_super_agent_data-bucket

915 kB

fuzzy-mittenz

published a bucket 7 days ago

BlackCubeBanditos/kilo_super_agent_data-bucket

915 kB

fuzzy-mittenz

in BlackCubeBanditos/README 7 days ago

World Model Requirements for the Field environment.

#2 opened 9 days ago by

fuzzy-mittenz

updated a collection 7 days ago

Models to Turn-ary Quant

Collection

2 items • Updated 7 days ago

fuzzy-mittenz

updated a collection 8 days ago

Local Prep Harness

Collection

5 items • Updated 8 days ago

shootstuff

updated a collection 9 days ago

Gram

Collection

Supporting Models for The God-Tier Relatively-Useless Agentic Mech-GRAM • 9 items • Updated 9 days ago • 1

fuzzy-mittenz

updated a collection 9 days ago

Local Prep Harness

Collection

5 items • Updated 8 days ago

fuzzy-mittenz

updated a collection 11 days ago

Local Prep Harness

Collection

5 items • Updated 8 days ago

Parveshiiii

posted an update 12 days ago

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193

Give your LLM a window to the web 🔎

I built Seekify, a Python tool that turns web search into clean, LLM-ready input for free.

Collecting sources for an LLM workflow usually means juggling search APIs, scraping pages, and cleaning up messy text. Seekify handles that in one place:

- No search API key needed
- Structured results with titles, URLs, and snippets
- Optional page text extraction so your model gets real content, not just links

Search the web, collect sources, and feed them straight into your pipeline, so you spend less time gathering data and more time building.

It's open source, and I'd love your feedback, ideas, and contributions.

👉 https://github.com/Parveshiiii/Seekify

If it's useful, a ⭐ on GitHub helps a lot!

fuzzy-mittenz

updated a collection 14 days ago

Gram

Collection

Supporting Models for The God-Tier Relatively-Useless Agentic Mech-GRAM • 9 items • Updated 9 days ago • 1

SeaWolf-AI

posted an update 16 days ago

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2,781

🚀 We pretrained a language model from scratch on an inference-only NPU, with real backpropagation.

🤗 Model: FINAL-Bench/VKORE-46M-NPU 📝 Technical write-up: https://huggingface.co/blog/FINAL-Bench/backpropagation-on-an-inference-only-npu-pretraini

"NPUs are inference-only, you can't train on them." We built a counterexample: VKORE-46M-NPU, trained from scratch on a first-gen (2025) inference NPU, 32 chips, no pretrained weights. A 46M decoder-only model (RoPE · SwiGLU · RMSNorm · GQA), char-level, TinyStories.

📏 Why trust it: a falling-loss curve proves nothing. So we trained the same model under identical init, data, and schedule on the NPU and on a GPU reference. · Loss trajectories correlate at 0.99 · Throughput converged to a comparable level The NPU backward pass and optimizer updates do numerically the same work as the GPU, not an approximation that merely trends down.

🧩 One accelerator now covers pretraining · LoRA · inference · quantization. We call this layer VKORE: "CUDA for NPUs."

⚖️ Public: weights, model card, NPU-vs-GPU comparison. Private (patent-pending, paper to follow): the core training technique. 46M char-level is toy scale on purpose: it proves the training path, not model quality. Larger models and real data are next.

Feedback from folks working on custom autograd backends and low-precision training kernels on non-GPU accelerators is very welcome. 🙌