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| title: OpenJev |
| emoji: 🎯 |
| colorFrom: green |
| colorTo: gray |
| sdk: static |
| app_file: index.html |
| pinned: false |
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| Give OpenJev text or a screenshot and your options. Get back a decision and a score. |
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| A decision model has to be small enough and fast enough to sit inside a product, and still be general: any situation, any set of choices, given at request time. Small task-specific models are fast, but they are not general. OpenJev is a 27B model that answers in one pass on a single GPU and comes close to Jev: 84.0% against 85.4% on our 10,000-question text benchmark. OpenJev Flash 9B is the smaller one: about 1.6x faster, 79.4% on the same 10,000 questions, and on par with Cloudflare's Clef-Flash and ahead of Kev-9B and Nimble 9B on JevBench. |
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| [Get OpenJev](https://huggingface.co/openjev/openjev) · [Build with Loop](https://www.loopai.com/) |
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| > **New: deploy OpenJev on Runpod Serverless.** Runpod published a step-by-step guide for running OpenJev on one H100, no GPU of your own needed: [How to deploy OpenJev on Runpod Serverless](https://www.runpod.io/blog/how-to-deploy-openjev-on-runpod-serverless). |
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| ## Choose your model |
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| - [OpenJev](https://huggingface.co/openjev/openjev): BF16 weights for text and screenshots. |
| - [OpenJev FP8](https://huggingface.co/openjev/openjev-FP8): GPU checkpoint for text and screenshots. |
| - [OpenJev MLX](https://huggingface.co/openjev/openjev-MLX): 8-bit text decisions on Apple silicon. |
| - [OpenJev MLX 4-bit](https://huggingface.co/openjev/openjev-MLX-4bit): text decisions on Apple silicon in about 15 GB. |
| - [OpenJev GGUF](https://huggingface.co/openjev/openjev-GGUF): llama.cpp builds; Q4_K_M runs text decisions on a 24 GB card (RTX 3090 / 4090), Q8_0 for 40 GB and up. |
| - [OpenJev Flash 9B](https://huggingface.co/openjev/OpenJev-Flash-9B): the 9B model, BF16 weights for text decisions, about 1.6x faster than OpenJev; on par with Clef-Flash, Kev-9B and Nimble on JevBench's public items. |
| - [OpenJev Flash 9B MLX](https://huggingface.co/openjev/OpenJev-Flash-9B-MLX): the 9B model in 8-bit for Apple silicon. |
| - [OpenJev Flash 9B MLX 4-bit](https://huggingface.co/openjev/OpenJev-Flash-9B-MLX-4bit), [FP8](https://huggingface.co/openjev/OpenJev-Flash-9B-FP8) and [GGUF](https://huggingface.co/openjev/OpenJev-Flash-9B-GGUF): the 9B for smaller Macs, vLLM without load-time quantization, and llama.cpp from 5.6 GB. |
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| **One server. Any model.** [openjev-server](https://huggingface.co/spaces/openjev/openjev-server) runs the same decision API on your own hardware, with OpenJev or any open model you choose, on a GPU or a Mac. `pip install` and one command to serve. Apache 2.0. |
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| The main model scores 84.0% on 10,000 text questions and 88.0% on 2,000 desktop screenshot next-action steps. [See the results and methods](https://huggingface.co/openjev/openjev#results). |
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| One forward pass per question with up to 52 choices. Open weights for research and non-commercial use under CC BY-NC 4.0. Commercial licence: [support@loopai.com](mailto:support@loopai.com). |
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| ## For enterprises |
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| **Policies change. The model does not need to.** The rules go in the request; the labels come back with probabilities, one pass each. Measured on generated cases with exact ground truth and one public dataset. [See the demos](https://github.com/loopai-hq/openjev-server/blob/master/demos/README.md). |
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| | **93.5%** | **200 of 200** | **95.0%** | |
| |---|---|---| |
| | policy decisions right, 200 cases under a nine-rule returns policy; 88.0% when it counts calendar days itself; majority baseline 42% | agent tool calls gated correctly under a seven-rule tool policy; 0 of 84 forbidden calls executed | prompt injections caught on 662 public messages; 97.7% recall at a 2% false-alarm rate | |
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| - **Runs where your data is.** Your GPU, your VPC or a Mac. Nothing leaves your network. |
| - **Probabilities you can act on.** Calibrated scores: auto-approve above a threshold, send the rest to a person. |
| - **One API, any model.** [openjev-server](https://huggingface.co/spaces/openjev/openjev-server) serves OpenJev or any open model behind the same interface. |
| - **Commercial use.** The weights are CC BY-NC 4.0. For a commercial licence, email [support@loopai.com](mailto:support@loopai.com). |
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| - **Text and screens.** Text, JSON, DOM or a screenshot, one endpoint. 88.0% on 2,000 desktop next-action steps. |
| - **Many questions, one call.** Route, score urgency and flag review in the same request; questions share the input but never see each other's answer. |
| - **Fast enough for the request path.** About 125 ms per decision on one H100, about 150 ms on a Mac, up to 52 options in one pass. |
| - **No lock-in.** Open weights you can audit, and the same API in front of any open model, so the vendor is never the bottleneck. |
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| **Why trust it.** Most small "decision models" only score well on their makers' own tests. On the same 10,000 public questions, same options, same order for every model: hosted Jev 85.4%, OpenJev 84.0%, the fine-tuned 9B we tested 75.7%. On par with the hosted model, eight points ahead of the small ones, and the method is public, so you can rerun it. |
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| OpenJev is an independent project, not affiliated with TypeSafe; Jev is their product. |
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