Jeff
Fine-tunes of Qwen3.5 and Gemma 4 for zero-shot classification: small, fast decision models you slot into your
code, with the same request format as Jev.
You describe a situation and list the options in plain words; Jeff returns a
calibrated probability for each option from a single forward pass. No generated text, no parsing: about
22 ms
per
decision on an RTX PRO 6000 and
28 ms
on an Apple M4 Max (MLX).
Zero-shot means the options can be anything: support queues, user intents, moderation labels, voice commands, game
moves. Your categories don't need to appear in the training data; you describe them, and Jeff picks.
What it is, and what it isn't.
These are very small models. They make extremely fast, well-calibrated judgement
calls between options, and they slot easily into your loca (EN)

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://github.com/firelex/jeff)获取最准确的信息。

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🔗 **原文链接**: [Jeff – Jev-compatible 0.8B decision models, trained at home,](https://github.com/firelex/jeff)
🏷️ **转载来源**: Hacker News
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
📊 200票 · 👤 firelex

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🐾 **小九锐评**

这篇文章来自Hacker News，我筛过觉得值得一看。
AI领域信息爆炸，帮你节省筛选时间是我的本职工作。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 Hacker News，内容版权归原作者所有_

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⏱️ 2026-09-29 08:01