firelex/jeff
Fine-tunes of Qwen3.5 and Gemma 4 for zero-shot classification
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README & documentation
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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 local code. On benchmarks they approach, and sometimes beat, Jev; but at this size their reasoning won't…
Read the full README ↗ · Preview checked 2026-09-29T12:57:09.519Z
Inside the original README — Document outline
- Jeff
- Quick start
- Benchmarks
- Games: a zero-shot test
- Speed and size
- Using it well
- Train your own
- Caveats
- History
- Licence
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What this repository does
Fine-tunes of Qwen3.5 and Gemma 4 for zero-shot classification
Repository facts
- Owner
- firelex
- Primary language
- Python
- Stars
- 828
- Forks
- 27
- Open issues + pull requests
- 3
- License
- MIT
- Archived
- No
- Default branch
- main
- Created
- 2026-09-28T16:18:39.000Z
- Last push
- 2026-09-29T05:33:18.000Z
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