SemIf

# SemIf (formerly OpenJev)

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**Semantic ifs from open models, on a 3090 at home.**

*Independent project; not affiliated with Jev or TypeSafe.*

**Wow! No waitlist.** [Run it in your browser today.](webgpu-demo/index.html)

[](demo/index.html)

*Same frozen 4B model · same state · same 21 questions · measured separately, aligned at t=0 in the replay*

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> **Independent research project.** SemIf was formerly called OpenJev. It is not affiliated with or endorsed by TypeSafe. Jev, TypeSafe, and other names and marks are the property of their respective owners. No infringement is intended.



Most agent decisions are small: *route this*, *retry that*, *does the evidence support X?* A chat model can answer them, but it spends time generating text that software immediately parses back into an `if` statement.

Jev is TypeSafe's closed service for runtime-defined semantic decisions. This project reproduces that **interface pattern** with open models; it does not reproduce Jev's undisclosed model or training.

This baseline reads typed option probabilities directly from a model. No answer sentence, JSON repair, or decoding loop.

### Latest changes — 2026-09-18

- Added MiniCPM5 2B and Qwen3.5 4B to the browser demo.
- Added **Unsloppify site**, a switch to a conventional interface.

## Quick start

**Apple Silicon:** use the native [MLX backend](docs/MLX.md) for direct scoring,
serial prefix reuse, and parallel shared-state decisions on macOS arm64.
Install `pip install -e '.[test,mlx]'` and add `--backend mlx` to the scorer command.

Python 3.10+, CUDA, and a GPU that can hold a 4B BF16 model:

```bash
python -m venv .venv
. .venv/bin/activate
export HF_HOME=/path/to/large-drive/huggingface
pip install -e '.[test]'
```

Run the owned examples:

```ba