new day, new models! Introducing the Auto series, a series of classifiers for determining if agentic tool calls are safe to run or not to prevent any harmful actions from happening. They have high performance compared to other LLMs commonly used for this task with incredible speed and small memory footprints. ProCreations/auto-1b (recommended generally, much higher accuracy) ProCreations/auto-0.4b (faster but worse) Comes with datasets as well (open source ftw)! A GitHub repo with pi extensions etc will come soon with this model.
We're building a dataset to study what humans actually consider AI slop.
SlopFinder shows you a random piece of AI-generated text and gives you one simple control: **how slop is it?** No categories. No complicated forms. Just vote and move on.
Every vote helps build the dataset. 🧩
How does it work? Samples are pulled from existing datasets, shown anonymously, and collected into our annotation pool. After enough votes, they're exported to Hugging Face for everyone to use.
This is an early MVP, so the dataset is small and the system is still evolving.
Today, we are announcing a brand-new series of SupraLabs models: Supra2 This series will feature various models, including such as: - 🐜 Supra2-Nano (0.4M) → The smallest Supra2 model. - 🤏 Supra2-Small (1.4M) → The tiny model that runs everywhere. - 💪 Supra2-Medium (25M) → Our medium class model in the Supra2 family. The powerful midsizer. - 🔥 Supra2-Pro (100M): base, instruct, reasoning, code, math and more! → The most capable model yet! A real allrounder for all your everyday tasks. - 🎨 Supra2-IMG → our generative text-to-image model ...and many more...
Current progress: - Nano (0.4M) and Small (1.4M): in training; almost done. Baseline set. - Medium (25M): coming soon... - Pro (100M): in training; finishes in 66 hours - Monday, 3rd August 2026, 12:00AM - IMG: coming soon...
You can support us with a like and follow if you want! Don't miss our next release! Stay tuned...
I went into this expecting to find a ~Q2 garbage dumpster but prism-ml/Ternary-Bonsai-27B-gguf is a slick feat of QAT engineering.
It is weaker then FP16 on a handful of tasks where quants usually degrade, as per their own paper the loss is "concentrated on sustained chains of reasoning / agentic" and in ReasonScape this bites on Sort, Shuffle and Dates, but counter-acting this are some noticeable improvements to thinking length without accuracy loss on several other tasks (Shapes, Cars).
I haven't had a chance to run the Binary yet, but PQ2 + Bonsai QAT are confirmed to be pretty darn impressive.