⬢github Python · 670 ★ +653 since we first saw it · pushed 2 h ago · MIT
firelex/jeff
Fine-tunes of Qwen3.5 and Gemma 4 for zero-shot classification
Jeff provides small (0.8B–2B) fine-tuned Qwen3.5 and Gemma 4 models for zero-shot classification: you describe options in plain language and it returns calibrated probabilities in a single forward pass, no text generation or parsing. It runs locally in ~22–30 ms, supports choice, yes/no, and score questions, and is Jev API-compatible.
Why now: Recently released and discussed on Hacker News as home-trained, ~30 ms decision models that approach Jev's published accuracy at a tiny size, built entirely on local hardware with synthetic data.
Who it is for: Developers who need fast, local, cheap classification routing or judgement calls inside applications without cloud LLM calls.
Stars over our 44 snapshots: 17 to 670, since 10 h ago.
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