OmniJev / OneJev

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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.
## 🔥 News
- 🔥 **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.
## Decision Index (public part)
## Other benchmarks
Accuracy (%). The OneJev test set is our own held-out set; the others are public.
## Speed
## Quick start
Choose one backend, then run the Python example below.
### Option A: PyTorch
For NVIDIA GPUs. Supports text, images and video.
 [code] 
### Option B: llama.cpp
For GGUF models. Supports text and images; use PyTorch for video. Install
llama.cpp first (`brew install llama.cpp` on macOS).
 [code] 
### Option C: Docker
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.
 [code] 
Force a backend with `./docker/start.sh `
(add `--build` to rebuild). Every profile serves the API and the playground on :8000.
On Windows only `torch` (NVIDIA) and `cpu` are available.
### Send a request
Both backends serve the same API at `http://localhost:8000`. In another terminal, run this example with your own
`screenshot.png`:
 [code] 
The API is compatible with TypeSafe System One. More examples: video,
curl, official TypeSafe SDK.
## Training
 [code] 
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.
### Fine-tune
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:
 [code] 
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:
 [code] 
## Friendly Links
- LINUX DO
- Jev
- Awesome JEV
- Awesome JEV Website
- PlayJev
## License
Apache 2.0
## Stars and downloads
## Citation
 [code]