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OmniJev
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Model Details
Model Provider
OmniJev
Model Tree
Qwen/Qwen3.5-0.8B
Input Modalities
Output Modalities
Supported Functionality
Accuracy in percent. The OneJev test set holds questions of the training kinds that no model saw in training. Jev 1.13's scores are its published ones, and it reads text only.
On one H200 with a 1280x720 screenshot, OneJev-0.8B answers 1 question in 31 ms and 10 questions in one request in 51 ms (5.1 ms per question).
bash
pip install "qev[torch] @ git+https://github.com/OmniJev/OneJev.git"qev serve --model OmniJev/OneJev-0.8Bpython
from qev import Client, Choice, Noulfrom qev.media import data_uri r = Client("http://localhost:8000").system_one( state={"task": "Pay the open invoice from ACME", "screen": "<image:1>"}, media=[{"type": "image", "data": data_uri("screenshot.png")}], questions={"done": Noul("The invoice has been paid"), "next": Choice("What should the agent do next?", {"click": "click an element", "stop": "stop"})},)The server speaks TypeSafe's System One API plus a media field for images and video. More examples, the latency
benchmark and the code are on GitHub.
OneJev-0.8B also runs on llama.cpp from the GGUF build by mradermacher, without PyTorch. Images work there; video needs the PyTorch server.
bash
brew install llama.cpppip install git+https://github.com/OmniJev/OneJev.gitqev serve --gguf mradermacher/OneJev-0.8B-GGUF:Q8_0| Model | Base | Weights |
|---|---|---|
| OneJev-0.8B | Qwen3.5-0.8B | 2.2 GB |
| OneJev-4B | Qwen3.5-4B | 10.4 GB |
| OneJev-9B | Qwen3.5-9B | 18.8 GB |
| OneJev-27B | Qwen3.8-27B | 54.7 GB |
| OneJev-27B-FP8 | OneJev-27B in 8-bit | 30.4 GB |
All sizes are in the OneJev collection.
bibtex
@misc{onejev2026, title = {{OneJev}: A Multimodal System One Decision Model}, author = {{OmniJev Team}}, year = {2026}, howpublished = {\url{https://github.com/OmniJev/OneJev}}}Apache 2.0
pip install "qev[torch] @ git+https://github.com/OmniJev/OneJev.git"qev serve --model OmniJev/OneJev-0.8Bfrom qev import Client, Choice, Noulfrom qev.media import data_uri r = Client("http://localhost:8000").system_one( state={"task": "Pay the open invoice from ACME", "screen": "<image:1>"}, media=[{"type": "image", "data": data_uri("screenshot.png")}], questions={"done": Noul("The invoice has been paid"), "next": Choice("What should the agent do next?", {"click": "click an element", "stop": "stop"})},)brew install llama.cpppip install git+https://github.com/OmniJev/OneJev.gitqev serve --gguf mradermacher/OneJev-0.8B-GGUF:Q8_0@misc{onejev2026, title = {{OneJev}: A Multimodal System One Decision Model}, author = {{OmniJev Team}}, year = {2026}, howpublished = {\url{https://github.com/OmniJev/OneJev}}}