| --- |
| title: Jevhakim |
| emoji: ⚖️ |
| colorFrom: green |
| colorTo: gray |
| sdk: gradio |
| sdk_version: 6.28.0 |
| python_version: "3.12" |
| app_file: app.py |
| short_description: Typed decisions for Arabic text, MSA and dialects |
| startup_duration_timeout: 1h |
| license: cc-by-nc-sa-4.0 |
| models: |
| - TigreGotico/jevhakim-4b |
| - TigreGotico/jevhakim-0.8b |
| - Qwen/Qwen3.5-0.8B-Base |
| - Qwen/Qwen3.5-4B-Base |
| datasets: |
| - TigreGotico/jevhakim-decisions |
| tags: |
| - decision-model |
| - arabic |
| - arabic-dialects |
| --- |
| |
| # Jevhakim |
|
|
| Jevhakim is a decision model for Arabic, both Modern Standard Arabic and the dialects. It reads a state, which is a |
| text or a JSON object, and a set of typed questions about it. It returns a probability for each option of each |
| question in one forward pass. It does not generate text. |
|
|
| This Space runs two models on ZeroGPU, in bf16. Pick one with the model selector. |
| [Jevhakim-4B](https://huggingface.co/TigreGotico/jevhakim-4b) is more accurate, and |
| [Jevhakim-0.8B](https://huggingface.co/TigreGotico/jevhakim-0.8b) is smaller. Both are |
| [Kev](https://github.com/jaredpalmer/kev) fine-tuned on the |
| [TigreGotico/jevhakim-decisions](https://huggingface.co/datasets/TigreGotico/jevhakim-decisions) dataset. |
|
|
| ## Request shape |
|
|
| A request follows Kev's System One format (`POST /v1/systemone`). Keys are English code identifiers: the state's |
| field names, the question ids and the option names. Values are Arabic. |
|
|
| ```json |
| { |
| "state": {"message": "اتخصم مني فلوس الباقة مرتين الشهر ده، وعايز الفلوس ترجعلي."}, |
| "questions": { |
| "category": {"type": "choice", "instructions": "إلى أي قسم ينتمي هذا الطلب؟", |
| "criteria": {"billing_payment": "الفواتير والدفع", "home_internet": "الإنترنت المنزلي", "devices": "الأجهزة"}}, |
| "needs_human": {"type": "noul", "instructions": "هل يحتاج هذا الطلب إلى موظف بشري؟"}, |
| "urgency": {"type": "score", "instructions": "كم هو ملحّ طلب العميل؟", |
| "criteria": ["غير مستعجل", "مستعجل قليلًا", "مستعجل", "عاجل جدًا"]} |
| } |
| } |
| ``` |
|
|
| There are three question types. A `choice` question picks one of its named options, and each option can have a |
| description. A `noul` question is yes or no, and the answer is p(yes). A `score` question picks a level on an ordered |
| list, and the answer is the expected level. |
|
|
| The **Questions** tab builds a request from a form. The **Raw JSON** tab takes a raw JSON request and shows the raw |
| JSON answer. The same two functions are on the Gradio API as `/decide` and `/systemone`. |
|
|
| ## Measured results |
|
|
| On the test split of 141,217 questions, Jevhakim-4B has accuracy 0.765, ECE 0.061 and Brier score 0.328. |
| Jevhakim-0.8B has accuracy 0.661, ECE 0.063 and Brier score 0.438. Stock Kev-0.8B has accuracy 0.503. On the |
| evaluation-only sources, which give no training rows, the accuracy is 0.727 for Jevhakim-4B, 0.585 for Jevhakim-0.8B |
| and 0.490 for stock Kev-0.8B. |
|
|
| ## How it runs |
|
|
| The Space installs Kev from GitHub at commit `1f04cfa28e6a708485b28a9585010b9e436b776e` and loads the model with |
| Kev's own loader, as `python -m kev.serve --run TigreGotico/jevhakim-0.8b` does. The probabilities use the calibration |
| temperature stored in the model's `head.pt`. |
|
|
| `JEVHAKIM_MODELS` names the models to serve, for example `JEVHAKIM_MODELS=Jevhakim-0.8B`. Off ZeroGPU, |
| `JEVHAKIM_DEVICE_4B` and `JEVHAKIM_DEVICE_08B` place each model, for example `JEVHAKIM_DEVICE_4B=cpu`. |
|
|
| ## Licence and limits |
|
|
| The models and the dataset are under CC BY-NC-SA 4.0, for non-commercial research only. Several training sources are |
| research-only. The instruction wording is model-written Modern Standard Arabic that no native speaker has reviewed. Do |
| not use the model for decisions with legal, medical, financial or employment consequences without human review. |
|
|
