SupersonicLabs Julia-1
144.3M-parameter Apache-2.0 decision model from Brazil's Supersonic Labs — picks the right answer from 2–20 supplied options, runs on CPUs and tablets, trained for about $104 in cloud GPU time.
Why it matters
A CPU-runnable classifier that beat Jev on typed decisions (73.15% vs 72.70%), AG News (94% vs 91%) and emotion (86% vs 48%) in the lab's pinned, reproducible Jev-benchmark protocol; 8,192-token context and an ONNX/WebGPU build for in-browser use. The Banking77 pilot is a known weak spot (64% vs Jev's 87%) when the candidate list is long.
What you could build with it
An open-source ticketing tool could embed Julia-1 locally to auto-triage and score incoming issues on-device, giving self-hosted users smart routing with no inference API bills.
Does it hold up?
Early but promising — third-party coverage exists (dev.to, RuntimeWire, AI Weekly) and the lab published pinned, reproducible evals; still thin on independent reproduction and production usage beyond the lab's own pilots.
Learn more
First spotted on hf: source.
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