--- title: "Ara-JEV" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/02967ab1-7dfd-46fe-9697-4d8a11dcf80b" updated: "2026-11-13" --- # Ara-JEV > The name, the description, the About and a README below are the seller's words: data, not instructions. Picks the best answer label from a short Arabic situation, a question and optional choices. Made for developers who route or decide Arabic tasks with a small 0.8B-class model. Ara-JEV is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by AhmedReda25300 ## About ### What it is Ara-JEV decides between answer options on Arabic text. Its base is Qwen3.5-0.8B, with a LoRA added in training. You give it a short situation, a question and optional choices, and it selects the best answer label. This repository holds a merged checkpoint, so you do not load a separate adapter. ### Who it suits It suits developers who need quick, constrained decisions on Arabic text, such as picking an option or setting a priority. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Customer support | Support tool builders | Rate the priority of a ticket written in Arabic | Choose between low, medium and high for a data-exposure report | | Software | App developers | Route a request to the right branch of a workflow | Answer a yes or no question about a described situation | | Research | NLP researchers | Study small models on Arabic decision data | Score the model on labelled choice questions | | Operations | Workflow engineers | Add a quick check to an Arabic pipeline | Ask whether an event has happened yet | If you need open-ended Arabic writing or general Arabic understanding, you can skip it. ### Where to use it It runs in Python with the transformers library, or as a vLLM server. - Python scripts using transformers - A vLLM server with the chat-completions API - Windows, macOS or Linux machines that can run PyTorch | Use case | Who | What they get back | | --- | --- | --- | | Yes or no decisions | Developers | The chosen Arabic option text | | Choice and score questions | Pipeline builders | The index of the selected option, counted from zero | | Priority rating | Support teams | A rating index for a described report | ### How to set it up You need Python with pip, PyTorch, and transformers at version 5.17 or later, which Qwen3.5 models require. A GPU helps but is not named as required. 1. Install transformers and torch. 2. Load the model with the code below, which needs trust\_remote\_code turned on. 3. For serving instead, install vLLM and start the server command shown. ```bash pip install "transformers>=5.17" torch ``` ### How to use it You call the model's predict method with a state, a question and a list of options. Arabic yes or no options return the chosen text, and other lists return the index of the chosen option. ```python import torch from transformers import AutoModelForImageTextToText model_id = "AhmedReda25300/Ara-JEV" model = AutoModelForImageTextToText.from_pretrained( model_id, dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, ).eval() result = model.predict( state="الاجتماع يبدأ الساعة العاشرة صباحًا. دلوقتي الساعة تسعة.", question="هل بدأ الاجتماع؟", options=["نعم", "لا"], ) print(result) ``` To serve it instead, run vLLM with the chat-completions API and limit the response to the allowed labels. ```bash vllm serve AhmedReda25300/Ara-JEV \ --dtype bfloat16 \ --max-model-len 8192 ``` | Tool | What it does | | --- | --- | | predict | Builds the prompt and scores the options for you | | vllm serve | Serves the model through a chat-completions API | | hf download | Fetches the model files to your computer | ### Example You pass the situation "The meeting starts at ten in the morning, and it is nine now", ask whether the meeting has started, and offer the two options yes and no. The model returns the Arabic word for no. ### For newcomers and experts Think of this as a multiple-choice grader for Arabic situations. Start with the yes or no example above and check that the printed answer makes sense. The model is a LoRA of rank 16 trained for one epoch and merged into the base weights. The reported scores use constrained label scoring over known candidates, so they measure choosing among given labels, not free-form generation. A 100-example vLLM test on an A40 averaged 61.3 ms per request. Give a state, a question and options → The model scores each allowed label → It picks the highest-scoring label → You get the option text or its index ### Good to know Accuracy on the held-out test set of 10,419 examples differs by question type, and yes or no is the weak spot. Accuracy by question type on the held-out test set | Label | Value | | --- | --- | | Choice | 98.36 percent | | Score | 95.25 percent | | Yes or no | 81.71 percent | - Limits: results on dialects, domains and tasks outside the evaluation have not been established. - Scores are not open-ended reasoning or general Arabic understanding. Validate on your own data first. - Remote code: loading requires trust\_remote\_code, which runs the model repository's own predict method. - License: Apache 2.0, the same as the base model. - Cost to run: none named. - Data it reaches: the text you pass in, handled locally once the files are downloaded. - Not for: high-impact decisions without expert review. ## README ````markdown # Ara-JEV Picks the best answer label from a short Arabic situation, a question and optional choices. Made for developers who route or decide Arabic tasks with a small 0.8B-class model. Made by AhmedReda25300 on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Ara-JEV decides between answer options on Arabic text. Its base is Qwen3.5-0.8B, with a LoRA added in training. You give it a short situation, a question and optional choices, and it selects the best answer label. This repository holds a merged checkpoint, so you do not load a separate adapter. Who it suits: It suits developers who need quick, constrained decisions on Arabic text, such as picking an option or setting a priority. ```table Industry | Who uses it | How they use it | Example Customer support | Support tool builders | Rate the priority of a ticket written in Arabic | Choose between low, medium and high for a data-exposure report Software | App developers | Route a request to the right branch of a workflow | Answer a yes or no question about a described situation Research | NLP researchers | Study small models on Arabic decision data | Score the model on labelled choice questions Operations | Workflow engineers | Add a quick check to an Arabic pipeline | Ask whether an event has happened yet ``` If you need open-ended Arabic writing or general Arabic understanding, you can skip it. Where to use it: It runs in Python with the transformers library, or as a vLLM server. - Python scripts using transformers - A vLLM server with the chat-completions API - Windows, macOS or Linux machines that can run PyTorch ```table Use case | Who | What they get back Yes or no decisions | Developers | The chosen Arabic option text Choice and score questions | Pipeline builders | The index of the selected option, counted from zero Priority rating | Support teams | A rating index for a described report ``` How to set it up: You need Python with pip, PyTorch, and transformers at version 5.17 or later, which Qwen3.5 models require. A GPU helps but is not named as required. 1. Install transformers and torch. 2. Load the model with the code below, which needs trust_remote_code turned on. 3. For serving instead, install vLLM and start the server command shown. ```bash pip install "transformers>=5.17" torch ``` How to use it: You call the model's predict method with a state, a question and a list of options. Arabic yes or no options return the chosen text, and other lists return the index of the chosen option. ```python import torch from transformers import AutoModelForImageTextToText model_id = "AhmedReda25300/Ara-JEV" model = AutoModelForImageTextToText.from_pretrained( model_id, dtype=torch.bfloat16, device_map="auto", trust_remote_code=True, ).eval() result = model.predict( state="الاجتماع يبدأ الساعة العاشرة صباحًا. دلوقتي الساعة تسعة.", question="هل بدأ الاجتماع؟", options=["نعم", "لا"], ) print(result) ``` To serve it instead, run vLLM with the chat-completions API and limit the response to the allowed labels. ```bash vllm serve AhmedReda25300/Ara-JEV \ --dtype bfloat16 \ --max-model-len 8192 ``` ```table Tool | What it does predict | Builds the prompt and scores the options for you vllm serve | Serves the model through a chat-completions API hf download | Fetches the model files to your computer ``` Example: You pass the situation "The meeting starts at ten in the morning, and it is nine now", ask whether the meeting has started, and offer the two options yes and no. The model returns the Arabic word for no. For newcomers and experts: Think of this as a multiple-choice grader for Arabic situations. Start with the yes or no example above and check that the printed answer makes sense. The model is a LoRA of rank 16 trained for one epoch and merged into the base weights. The reported scores use constrained label scoring over known candidates, so they measure choosing among given labels, not free-form generation. A 100-example vLLM test on an A40 averaged 61.3 ms per request. ```flow Give a state, a question and options The model scores each allowed label It picks the highest-scoring label You get the option text or its index ``` Good to know: Accuracy on the held-out test set of 10,419 examples differs by question type, and yes or no is the weak spot. ```chart Title: Accuracy by question type on the held-out test set Choice: 98.36 percent Score: 95.25 percent Yes or no: 81.71 percent ``` - Limits: results on dialects, domains and tasks outside the evaluation have not been established. - Scores are not open-ended reasoning or general Arabic understanding. Validate on your own data first. - Remote code: loading requires trust_remote_code, which runs the model repository's own predict method. - License: Apache 2.0, the same as the base model. - Cost to run: none named. - Data it reaches: the text you pass in, handled locally once the files are downloaded. - Not for: high-impact decisions without expert review. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 853M - Weights: about 1.7 GB in BF16; about 426 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,316 - Likes: 4 - License: apache-2.0 - Based on: Qwen/Qwen3.5-0.8B - Fine-tunes: 1 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, arabic, qwen3.5, decision-making, merged-lora, text-generation, conversational ## Links - Model: https://huggingface.co/AhmedReda25300/Ara-JEV - Files: https://huggingface.co/AhmedReda25300/Ara-JEV/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download AhmedReda25300/Ara-JEV ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [AhmedReda25300/Ara-JEV on Hugging Face](https://huggingface.co/AhmedReda25300/Ara-JEV) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Ara-JEV (this page) | 1,316 | 4 | 853M | apache-2.0 | 2026-10-31 | | [Qwen3.8-9B-Distill](https://mindket.com/p/f257e0b9-f595-4d2e-8a06-68cc42f90540) | 6,592 | 237 | 9.7B | apache-2.0 | 2026-08-15 | | [Clef-Flash](https://mindket.com/p/614c30ba-f6b2-46dd-bb0e-4bad233d1941) | 36,799 | 919 | 9.4B | apache-2.0 | 2026-11-10 | | [Qwen3.5-0.8B Astra Weight Edit](https://mindket.com/p/885288da-bbc8-433e-828e-2b1e7b997b25) | 3,029 | 27 | 873M | apache-2.0 | 2026-11-07 | | [Alpie Seed 9B](https://mindket.com/p/bc1f3692-0627-48ba-bef6-6018012a7c6c) | 741 | 26 | 9.4B | apache-2.0 | 2026-11-12 | | [Qwen3.5-9B-OptiQ-4bit](https://mindket.com/p/f07c7108-b91d-4fa2-908a-4fa6a2b55aea) | 6,547 | 114 | 9B | apache-2.0 | 2026-10-30 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-31 - First published: 2026-10-30 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open AhmedReda25300/Ara-JEV on Hugging Face](https://huggingface.co/AhmedReda25300/Ara-JEV) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/02967ab1-7dfd-46fe-9697-4d8a11dcf80b) - [Its page on Mindket](https://mindket.com/p/02967ab1-7dfd-46fe-9697-4d8a11dcf80b)