Jeff: 0.8B decision models trained at home, ~30 ms inference

Jeff – Jev-compatible 0.8B decision models, trained at home, ~30 ms

Jeff is a set of 0.8B parameter models fine-tuned from Qwen3.5 and Gemma 4 for zero-shot classification. Trained on consumer hardware at home, it runs inference in ~30 ms and is Jev-compatible. The post doesn't disclose dataset size or benchmarks, but the GitHub repo includes code and weights. For teams needing lightweight decision pipelines, the latency and size are practical.