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OneJev is a multimodal System One decision model based on Qwen. It returns calibrated probabilities for typed questions about screenshots, photos, videos and text in a single forward pass. Available in 0.8B, 4B, 9B and 27B.

• 🔥 2026-10-17 We released OneJev 1.6 on Hugging Face and ModelScope. Welcome to have a try.
• 2026-10-13 We released OneJev 1.5. Welcome to have a try.
• 2026-09-28 We released the online demo, 👉 Click me.
• 2026-09-27 We released OneJev and the OneJev dataset.

Accuracy (%). The OneJev test set is our own held-out set; the others are public.

Choose one backend, then run the Python example below.

For NVIDIA GPUs. Supports text, images and video. Flide is our inference engine for decision models.

uv venv -p 3.12 && source .venv/bin/activate
uv pip install flide torchvision
flide serve --model OmniJev/OneJev1.6-0.8B --port 8000

For GGUF models. Supports text and images; use Flide for video. Install llama.cpp first (brew install llama.cpp on macOS).

pip install git+https://github.com/OmniJev/OneJev.git
qev serve --gguf mradermacher/OneJev1.6-0.8B-GGUF:Q8_0 --port 8000

The repo ships a Compose stack that detects your hardware and starts one backend. The llama.cpp profiles download mradermacher/OneJev1.6-0.8B-GGUF:Q8_0 on first start; set QEV_GGUF in docker/.env for another size or quant.

cp docker/.env.example docker/.env   # set HF_TOKEN for gated/private repos
./docker/start.sh                    # auto-detect (Windows: .\docker\start.ps1)
./docker/start.sh logs               # follow logs
./docker/start.sh down               # stop every profile

Force a backend with ./docker/start.sh <torch|rocm|vulkan|gguf|gguf-rocm|cpu> (add --build to rebuild). Every profile serves the API and the playground on :8000. On Windows only torch (NVIDIA) and cpu are available.

The server answers at http://localhost:8000. In another terminal, run this example with your own screenshot.png:

IMG=$(base64 -w0 screenshot.png)
curl http://localhost:8000/v1/systemone -H "Content-Type: application/json" -d @- <<EOF
{
  "state": {"task": "Pay the open invoice from ACME", "screen": "<image:1>"},
  "media": [{"type": "image", "data": "data:image/png;base64,$IMG"}],
  "questions": {
    "done": {"type": "noul", "instructions": "The invoice has been paid"},
    "next": {"type": "choice", "instructions": "What should the agent do next?",
             "criteria": {"click": "click an element", "type": "type text", "scroll": "scroll", "stop": "stop"}},
    "progress": {"type": "score", "instructions": "How far along is the task?",
                 "criteria": ["not started", "halfway", "almost done", "done"]}
  }
}
EOF

answers.done.noul is the probability of yes, answers.next.probabilities gives one probability per option and answers.progress.score is the expected level.

The API is compatible with TypeSafe System One. More examples: video, curl, official TypeSafe SDK.

git clone https://github.com/OmniJev/OneJev.git
cd OneJev
pip install -e ".[train]"

# Choose A or B. A is enabled below.
# A. Demo: 100 examples with images (23 MB)
hf download OmniJev/OneJev-Data sample/sample-100.parquet --repo-type dataset --local-dir data/onejev
python -m train.unpack data/onejev --sample

# B. Full dataset: 94,707 examples (17.7 GB). Uncomment these two lines instead of A.
# hf download OmniJev/OneJev-Data --repo-type dataset --include "data/*.parquet" --local-dir data/onejev
# python -m train.unpack data/onejev

Either option creates data/onejev/train.jsonl and extracts images and video frames into data/onejev/media/.

OneJev-Data releases 94,707 of the original 99,193 training questions; the remaining sources do not permit redistribution.

The four models fine-tune Qwen3.5-0.8B, Qwen3.5-4B, Qwen3.5-9B and Qwen3.8-27B for one epoch with the vision tower frozen. The configs use a learning rate of 5e-6, a 16,384-token limit, and cross-entropy plus Brier loss over answer probabilities. To train the 4B model on four GPUs:

torchrun --nproc-per-node 4 -m train.sft --config train/configs/onejev_4b_full.yaml

Configs: 0.8B · 4B · 9B · 27B. Set --nproc-per-node to your GPU count; the 9B and 27B configs enable FSDP to shard model and optimizer state. For your own data, change train and media_root in the config.

The final 4B checkpoint is saved to train/runs/onejev_4b/final. Start it with:

flide serve --model train/runs/onejev_4b/final --format onejev --port 8000

• LINUX DO
• Jev
• Awesome JEV
• Awesome JEV Website
• PlayJev

Apache 2.0

@misc{onejev2026,
  title        = {{OneJev}: A Multimodal System One Decision Model},
  author       = {{OmniJev Team}},
  year         = {2026},
  howpublished = {\url{https://github.com/OmniJev/OneJev}}
}