SemIf
15.5rank #45 · Capability #27
Model guide · Formerly OpenJev · MIT logit reader on Qwen3.5-4B · updated October 7, 2026
SemIf
Semantic ifs from a frozen open model, with no generation at all.
SemIf takes an unstructured state, criteria you write at request time and a set of typed options, runs one forward pass through an open model and reads the native logits for the option tokens. Nothing is generated, so there is no sentence to parse and no malformed JSON to retry. It was called OpenJev until it was renamed, which is why older benchmark tables and blog posts still use that name. The code is MIT; the weights keep their upstream licences.
SemIf is an independent project by TheoLeeCJ. Jev AI does not host it; this page is a guide to what it is and how to run it yourself.
- Developer
- TheoLeeCJ (independent)
- Primary model
- Qwen3.5-4B, the configuration JevBench measured
- Also runs on
- Qwen3-0.6B · MiniCPM5-2B · Qwen3-Reranker-4B
- Licence
- MIT code; upstream model licences
- Deployment
- Self-hosted only, plus a WebGPU browser demo
- Published hardware
- RTX 3090
- Throughput
- 20.03 decisions per second with parallel suffix reuse
- Calibration
- Conditional on your options; fit per workload
SemIf on JevBench v1.6.0
One benchmark measured every system under one method and ranked 92. The full board is on the JevBench results page.
Jev 1.13.0
71.1rank #2 · Capability #3
IntelligenceHow often it picks the right answer, half from sealed decisions
CalibrationWhether 0.8 really means about 80%
SpeedMeasured response time
Open half 30.4 · sealed half 23.8; chance-corrected by request type: choice 47.6, yes/no 8.3, score 25.3. The composite also weighs a fourth axis, cost, which these bars leave out. Self-hosted latency is adjusted by the benchmark to approximate production load.
What SemIf is
SemIf reproduces Jev’s interface pattern, not Jev’s undisclosed model or training: unstructured state, runtime criteria, typed options, and a probability for every option from one forward pass. The project is explicit about that distinction.
The speed argument is the strongest part of the project. On an RTX 3090 with a frozen state and 21 binary criteria, reading typed logits directly takes 1.023 seconds against 5.332 seconds to generate the same JSON array. With parallel suffix reuse the author measures 20.03 decisions per second against 2.33 for the autoregressive path.
On accuracy the author is careful: on a 102-row subset of TypeSafe’s cases, direct logits reach 0.845 modal agreement against Jev’s published 0.883, and balanced accuracy on authored decisions is 0.813. The README notes that this covers 102 aligned rows, not TypeSafe’s full 711-row aggregate, and asks you to calibrate and validate per workload.
Where it stands on JevBench
JevBench v1.6.0 ranks SemIf on Qwen3.5-4B #45 of 92 at 15.5, measured on an RTX 5090. Speed is 90.8 and calibration 83.6; intelligence is 27.1, with 23.8 on the sealed half, and yes/no questions score 8.3 above chance. In v1.5.6 it was #14; the full re-measure on the new pool moved it down sharply. Jev is #2 at 71.1 with intelligence 62.4.
JevK5 builds on the same readout and base model, adds a distilled LoRA and a fitted temperature, and ranks #18; Plumb-4B, fine-tuned from JevK5, ranks #10. If you like SemIf’s approach but need more accuracy, that is the lineage to follow.
Run SemIf
Clone the repository, install it against a GPU with the Qwen3.5-4B weights, and call it with a state, criteria and options. There is also a WebGPU demo that runs the smaller models in a browser tab.
git clone https://github.com/TheoLeeCJ/SemIf
cd SemIf
# Follow README.md to install and choose a model (Qwen3.5-4B is the primary baseline).
# A request supplies the state, runtime criteria and typed options; the response
# is a probability per option, read from the model's logits in one pass.- Calibrate on your own labelled data: the probabilities are conditional on the options you supply, and the README asks for per-workload validation.
- Production throughput assumes a GPU you keep running; smaller supported models run on less.
- The WebGPU demo is for trying the idea, not for serving traffic.
Limits to plan around
From the project’s own documentation and JevBench v1.6.0.
- Intelligence 27.1 in JevBench v1.6.0, and 8.3 above chance on yes/no questions.
- No post-training for calibration; fit a temperature per workload.
- Self-hosted only: there is no SemIf service to call.
Measure it against Jev on your own cases
The honest comparison is on your data, not on either scoreboard. Running Jev here takes a minute with no setup; if the answers on your awkward cases are close enough, SemIf is a legitimate way to run typed decisions on hardware you already own.
SemIf FAQ
Is SemIf the same as OpenJev?
SemIf is the current name of the project formerly called OpenJev, by TheoLeeCJ. Other projects also use the openJev name, including razorback16’s DiffusionGemma server and openJev Verdict, and they are unrelated codebases by different authors.
What is SemIf?
An MIT-licensed project that reads typed option probabilities straight from the logits of a frozen open model such as Qwen3.5-4B, in one forward pass, with the state, criteria and options supplied at request time. It reproduces Jev’s interface, not Jev’s model.
How accurate is SemIf?
In JevBench v1.6.0 it ranks #45 of 92 at 15.5 with intelligence 27.1 against Jev’s 62.4. On the author’s 102-row subset of TypeSafe’s cases it reaches 0.845 modal agreement against Jev’s published 0.883.
What hardware does SemIf need?
The published numbers are on an RTX 3090 with a 4B model. Qwen3-0.6B and MiniCPM5-2B run on less, and there is a WebGPU browser demo, but production throughput assumes a GPU you keep running.
Does SemIf give calibrated probabilities?
It returns probabilities conditional on the options you supply and the README asks you to calibrate and validate per workload. JevBench v1.6.0 scores its calibration at 83.6 against Jev’s 90.6.
More model guides
Every figure on this page is quoted from a published source and was read on October 7, 2026.
- JevK5The fastest open 4B on the board, with the weights published.
- OpenJevOne name, ten projects. Find yours, then run the drop-in.
- CygnetSeventh on the board, and nothing about the model is trained.
- Jev 1.13.0The hosted model every project here is measured against.
- Jev-OmniGemma 4 12B decision model with image input.
Sources
SemIf and every other product named on this page belongs to its respective owner and is not affiliated with Jev AI. Figures are quoted from the sources above as they read on October 7, 2026 and can change.