Jeff – Jev-compatible 0.8B decision models, trained at home, ~30 ms
Jeff – Jev-compatible 0.8B decision models, trained at home, ~30 ms firelex / jeff Public Notifications You must be signed in to change notification settings Fork 1 Star 20 Branches Tags Open more actions menu Latest commit History 6 Commits 6 Commits Folders and files Name Name Last commit message Last commit date assets assets docs docs scripts scripts src/ jeff src/ jeff tests tests videos videos .gitattributes .gitattributes .gitignore .gitignore LICENSE LICENSE README.md README.md pyproject.toml pyproject.toml uv.lock uv.lock Repository files navigation 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.
This OpenSourceRelease is relevant to the technology intelligence record because it involves Apple, Hugging Face, NVIDIA, qwen. The source article should remain the factual reference for follow-up coverage.
- firelex / jeff Public Notifications You must be signed in to change notification settings Fork 1 Star 20 Branches Tags Open more actions menu Latest commit History 6 Commits 6 Commits Folders and files Name Name Last commit message Last commit date assets assets docs docs scripts scripts src/ jeff src/ jeff tests tests videos videos .gitattributes .gitattributes .gitignore .gitignore LICENSE LICENSE README.md README.md pyproject.toml pyproject.toml uv.lock uv.lock Repository files navigation 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.