@Zefan_Cai: Spot the wrong target before you hit Confirm. Try the Open-Jev-27B-v1.1 demo: click between two examples and see how th…
Summary
Open-Jev-27B-v1.1 is an open-source AI model with a LoRA adapter, achieving 85.28% accuracy on JevBench and featuring interactive demos for various tasks.
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Cached at: 09/23/26, 06:11 PM
Spot the wrong target before you hit Confirm. Try the Open-Jev-27B-v1.1 demo: click between two examples and see how the model chooses an action. No code. No setup. An interactive replay of real model outputs. Try it ↓ https://zefan-cai.github.io/open-jev/v1-1/
Open-Jev-27B-v1.1 · Same preview. Different target.
Source: https://zefan-cai.github.io/open-jev/v1-1/ Open-Jev-27B-v1.1 / New model + new demo
The preview points torecord_A. A candidate action now targetsrecord_B. Inspect how two saved 27B v1.1 predictions select among the supplied options.
Rank-8 LoRA adapter + FP32 decision head over a pinned Qwen3.8-27B base. Base weights are required separately.
Illustrative UI from synthetic text-state records. No browser actions executed. Playback is edited and does not measure inference latency.
Public JevBench / 231 tasks
80 out of 111 on Hard. One answer behind Jev.
Open-Jev-27B-v1.1 scores197/231 overall (85.28%)and80/111 Hard (72.07%). Jev scores 200/231 overall and 81/111 Hard.
This is the public 231-task subset of the 534-task benchmark. Model scale, data and prior training differ across Open-Jev versions. Shared multi-GPU evaluation times do not establish single-GPU or HTTP/API latency.
127,787Complete internal Test/OOD rows audited
**96.65%**Expanded Test · 41,357 / 42,789 hard labels
**96.44%**Expanded OOD · 80,934 / 83,924 hard labels
All 43,301 Test and 84,486 OOD rows were evaluated; accuracy uses hard labels. The public dataset is a 326,619-row redistributable projection, excluding 2,053 Wiki records. It is not the complete internal corpus.See all four old/expanded Test/OOD columns·Audit and protocol.
New / Four interactive game demos
Your move. Then the model’s.
Try Snake, tic-tac-toe, Box Runner and Tile Platformer. Choose an action, then inspect the realOpen-Jev-27B-v1.1probabilities across 16 independent recorded game states, including three model/reference disagreements.
Interactive snapshots of saved decisions; no live model inference or new gameplay rollout.
Enter the game arcade ↗ Try text classification in your browser
Bring your own categories.
The interactive workbench runsOpen-Jev-2B on CPU. Paste messages or import a CSV, define labels, then download the results.
Open the Open-Jev-2B workbench ↗
Inspect the decision. Build your own.
Open-Jev-27B-v1.1 is a LoRA adapter and decision head. These selected examples do not establish a general task success rate or a safety guarantee.
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