#99th of 42 in Open models
kev
Small decision models answering yes/no, choice and score questions over text
- Stars
- 9.4k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- Python
0 on 16 Sept 20269,422 on 21 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Kev is a family of four fine-tuned Qwen-based models (0.8B, 4B, 9B, 27B) that answer yes/no, multiple-choice and rating questions about a text and return calibrated probabilities. The server exposes an API matching TypeSafe's System One, so the TypeSafe Python SDK works against it. The repo includes training, evaluation and serving code, plus a Modal deploy script and fine-tuning skill.
Who it is for: Engineers routing or classifying text who want a self-hosted Jev-style API
Strengths
- Calibrated probabilities: each checkpoint ships with a fitted temperature
- Works with the TypeSafe Python SDK unchanged via the /v1/systemone endpoint
- Four sizes; 0.8B runs on a 4 GB GPU, 27B scores within a point of Jev on new sources
- Fine-tune from released checkpoints; about $1 for a Kev-4B run on a Modal H100
Weaknesses
- Not on PyPI; install from source with uv, Python 3.12 or 3.13 only
- Kev-27B is 51 GB of full weights and needs an 80 GB-class GPU
- Trails Jev on harder knowledge tasks, e.g. MMLU-Pro 0.675 vs 0.840 for Kev-27B
- Validated context is 8,192 tokens for 0.8B to 9B, despite a 65,536 server limit
What it needs
- GPU optional
- Needs Modal (optional, for deploy and fine-tune skill), uv, Hugging Face Hub
- Models: Kev-0.8B, Kev-4B, Kev-9B, Kev-27B, Qwen3.5 base
- port 8009
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