Jev-Omni Scores Decisions Across Text, Images, Audio, and Video in Milliseconds
A 12B multimodal classifier fine-tuned from Gemma 4 that returns calibrated probabilities across text, image, audio and video inputs.
Read the full story at AlphaSignal ↗
Timeline · 1 report
- 2026-09-29 00:57 · AlphaSignal
Jev-Omni Scores Decisions Across Text, Images, Audio, and Video in Milliseconds
More stories
- Trained locally: ultra-fast 0.8B/2B System 1 decision models that match Jev on benchmarks and Doom, ~30 ms per decision (open weights) — r/LocalLLaMA
- OpenDecider: distilling calibrated "System One" decision models from open teachers, evaluated head-to-head against Laya and TypeSafe's Jev — r/machinelearningnews
- TensorSharp Jev requests can now combine documents, images, video, and audio — r/LocalLLM
- How JEV works internally — r/learnmachinelearning
- New frontier AI models, TypeSafe’s Jev AI, & NASA’s IBM collab — Mixture of Experts (IBM)
- LLM or JEV? Why not both? - introducing a hybrid Gemma4 approach — r/LocalLLM
- How long until OpenAI release their version of Jev? — r/AI_Agents
- Supersonic Labs Releases Julia 1: A 144.3M-Parameter Open Decision Model That Runs on a CPU — MarkTechPost
Get the daily brief of stories like this at 6:30 every morning →