--- title: "OneJev1.6-4B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/54898cc3-d040-488d-ae95-b4e9f0e49de4" updated: "2026-11-13" --- # OneJev1.6-4B > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads a screenshot, photo, video or text with a few typed questions and returns a probability for each answer option in one pass. Built for developers of screen-driving agents. OneJev1.6-4B 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 OmniJev ## About ### What it is OneJev1.6-4B is a multimodal decision model built on Qwen3.5-4B. You give it a screenshot, photo, video or text plus a few typed questions, and it scores every answer option in one pass. It was fully fine-tuned on about 440,000 questions from agent runs, videos, images and public decision datasets, so it fits jobs like judging whether a task is done. ### Who it suits Developers building agents that read a screen and pick the next action are the main fit. Teams that need a quick yes or no on a finished task can use it too, and so can researchers who compare small decision models. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Agent developers | Ask which action a screen agent should take next | Choose between click, type, scroll or stop from a screenshot | | Operations | Automation teams | Check whether a task has finished | Ask whether an open invoice has been paid | | Research | Model researchers | Compare small decision models on a shared board | Score it against other models of similar size on Decision Index | If you want long written answers or open chat, you can skip it. ### Where to use it Use it on any computer that runs Python. - Windows, macOS or Linux with Python - A local server started with the flide tool - The Hugging Face transformers library | Use case | Who | What they get back | | --- | --- | --- | | Screen agent steering | Agent developers | A probability for click, type, scroll and stop | | Task completion check | Automation builders | A yes probability that the task is done | | Progress tracking | Workflow teams | An expected level of how far along a task is | ### How to set it up You need a computer with Python and some free disk space. - Windows, macOS or Linux, with Python (the example uses Python 3.12) - The uv tool, which the example uses to make its virtual environment - Room for the weights, about 9.1 GB in BF16 or about 2.3 GB at 4-bit as an estimate 1. Install the Hugging Face hub package and download the model files if you want a local copy. 2. Create a Python 3.12 environment and activate it. 3. Install flide and torchvision in that environment. ```sh pip install -U huggingface_hub hf download OmniJev/OneJev1.6-4B ``` ```bash uv venv -p 3.12 && source .venv/bin/activate uv pip install flide torchvision ``` ### How to use it Start the server with the model name, then send a JSON request from another terminal. The request holds a state with the task and the screen, the media such as a screenshot, and your typed questions. ```bash flide serve --model OmniJev/OneJev1.6-4B ``` | Tool | What it does | | --- | --- | | noul question | Returns the probability of yes for a statement | | choice question | Returns one probability for each option you list | | score question | Returns the expected level on a scale you define | The request goes to the server's systemone endpoint. Answers come back under an answers key, one entry per question. ### Example You send a screenshot of an invoice page, the task of paying ACME's open invoice, and three questions. Is the task done, what should the agent do next among click, type, scroll or stop, and how far along is it. The server returns a yes probability for done, one probability for each next action, and an expected progress level. ### For newcomers and experts This model looks at a picture or a short video and answers your questions with probabilities instead of sentences. A good first try is to download the files with the two Hugging Face commands, start the server, and ask one yes or no question about a screenshot of your own. Under the hood it is a full fine-tune of Qwen3.5-4B, about 4.5B parameters, that gives a calibrated probability per option in a single forward pass rather than writing text. One pass covers every typed question you attach. In return, you phrase each question as a yes or no check, a choice or a score. Send a screenshot, photo, video or text → Add typed questions with their options → The model runs one forward pass → Read a probability for each option ### Good to know These scores are from Decision Index 0.3, using its public portion, and this model has the top overall score among those compared. Decision Index public part score | Label | Value | | --- | --- | | OneJev1.6-4B | 62.01 points | | Perplexity Decider Flash v1.2 | 60.06 points | | CoCo-Decision-4B 2.3.0 | 55.38 points | | vLLM-SR d3-mini | 54.90 points | | OneJev1.5-4B | 54.89 points | | RSI-Jev v6.1 VL 4B | 50.98 points | - License: Apache 2.0 - Cost to run: none named. The weights take about 9.1 GB in BF16 or about 2.3 GB at 4-bit, as an estimate - Data it reaches: the screenshots, photos, videos and text you send in a request. The source names no account or web service - Not for: the source names no exclusions ## README ````markdown # OneJev1.6-4B Reads a screenshot, photo, video or text with a few typed questions and returns a probability for each answer option in one pass. Built for developers of screen-driving agents. Made by OmniJev 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: OneJev1.6-4B is a multimodal decision model built on Qwen3.5-4B. You give it a screenshot, photo, video or text plus a few typed questions, and it scores every answer option in one pass. It was fully fine-tuned on about 440,000 questions from agent runs, videos, images and public decision datasets, so it fits jobs like judging whether a task is done. Who it suits: Developers building agents that read a screen and pick the next action are the main fit. Teams that need a quick yes or no on a finished task can use it too, and so can researchers who compare small decision models. ```table Industry | Who uses it | How they use it | Example Software | Agent developers | Ask which action a screen agent should take next | Choose between click, type, scroll or stop from a screenshot Operations | Automation teams | Check whether a task has finished | Ask whether an open invoice has been paid Research | Model researchers | Compare small decision models on a shared board | Score it against other models of similar size on Decision Index ``` If you want long written answers or open chat, you can skip it. Where to use it: Use it on any computer that runs Python. - Windows, macOS or Linux with Python - A local server started with the flide tool - The Hugging Face transformers library ```table Use case | Who | What they get back Screen agent steering | Agent developers | A probability for click, type, scroll and stop Task completion check | Automation builders | A yes probability that the task is done Progress tracking | Workflow teams | An expected level of how far along a task is ``` How to set it up: You need a computer with Python and some free disk space. - Windows, macOS or Linux, with Python (the example uses Python 3.12) - The uv tool, which the example uses to make its virtual environment - Room for the weights, about 9.1 GB in BF16 or about 2.3 GB at 4-bit as an estimate 1. Install the Hugging Face hub package and download the model files if you want a local copy. 2. Create a Python 3.12 environment and activate it. 3. Install flide and torchvision in that environment. ```sh pip install -U huggingface_hub hf download OmniJev/OneJev1.6-4B ``` ```bash uv venv -p 3.12 && source .venv/bin/activate uv pip install flide torchvision ``` How to use it: Start the server with the model name, then send a JSON request from another terminal. The request holds a state with the task and the screen, the media such as a screenshot, and your typed questions. ```bash flide serve --model OmniJev/OneJev1.6-4B ``` ```table Tool | What it does noul question | Returns the probability of yes for a statement choice question | Returns one probability for each option you list score question | Returns the expected level on a scale you define ``` The request goes to the server's systemone endpoint. Answers come back under an answers key, one entry per question. Example: You send a screenshot of an invoice page, the task of paying ACME's open invoice, and three questions. Is the task done, what should the agent do next among click, type, scroll or stop, and how far along is it. The server returns a yes probability for done, one probability for each next action, and an expected progress level. For newcomers and experts: This model looks at a picture or a short video and answers your questions with probabilities instead of sentences. A good first try is to download the files with the two Hugging Face commands, start the server, and ask one yes or no question about a screenshot of your own. Under the hood it is a full fine-tune of Qwen3.5-4B, about 4.5B parameters, that gives a calibrated probability per option in a single forward pass rather than writing text. One pass covers every typed question you attach. In return, you phrase each question as a yes or no check, a choice or a score. ```flow Send a screenshot, photo, video or text Add typed questions with their options The model runs one forward pass Read a probability for each option ``` Good to know: These scores are from Decision Index 0.3, using its public portion, and this model has the top overall score among those compared. ```chart Title: Decision Index public part score OneJev1.6-4B: 62.01 points Perplexity Decider Flash v1.2: 60.06 points CoCo-Decision-4B 2.3.0: 55.38 points vLLM-SR d3-mini: 54.90 points OneJev1.5-4B: 54.89 points RSI-Jev v6.1 VL 4B: 50.98 points ``` - License: Apache 2.0 - Cost to run: none named. The weights take about 9.1 GB in BF16 or about 2.3 GB at 4-bit, as an estimate - Data it reaches: the screenshots, photos, videos and text you send in a request. The source names no account or web service - Not for: the source names no exclusions ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 4.5B - Weights: about 9.1 GB in BF16; about 2.3 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,909 - Likes: 8 - License: apache-2.0 - Based on: Qwen/Qwen3.5-4B - Quantized versions: 2 - Fine-tunes: 1 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, onejev, system-one, decision-model, calibration, multimodal, gui-agent, video, conversational ## Links - Model: https://huggingface.co/OmniJev/OneJev1.6-4B - Files: https://huggingface.co/OmniJev/OneJev1.6-4B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download OmniJev/OneJev1.6-4B ``` 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 [OmniJev/OneJev1.6-4B on Hugging Face](https://huggingface.co/OmniJev/OneJev1.6-4B) ## 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 | | --- | --- | --- | --- | --- | --- | | OneJev1.6-4B (this page) | 1,909 | 8 | 4.5B | apache-2.0 | 2026-11-10 | | [OneJev1.6-0.8B](https://mindket.com/p/7b44bdb4-74c1-4e3b-b0c2-32e28fc4663f) | 1,885 | 6 | 853M | apache-2.0 | 2026-11-10 | | [OneJev1.6-27B](https://mindket.com/p/cf8a383c-4770-4ae2-b940-4df48666dae9) | 528 | 5 | 27.4B | apache-2.0 | 2026-11-10 | | [OneJev1.6-4B-GGUF](https://mindket.com/p/b8361417-2c40-44d7-b244-622b86f6689c) | 4,292 | 2 | 4.2B | apache-2.0 | 2026-10-17 | | [d3-flash](https://mindket.com/p/9f4e35f9-58d2-4c23-bfde-20d3530ef7ce) | 103,577 | 114 | 8.4B | apache-2.0 | 2026-11-02 | | [d3-mini](https://mindket.com/p/4e08c56c-92f6-4e07-8884-2cd16598fc8b) | 53,115 | 75 | 4.5B | 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: 2026-11-10 - First published: 2026-10-16 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open OmniJev/OneJev1.6-4B on Hugging Face](https://huggingface.co/OmniJev/OneJev1.6-4B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/54898cc3-d040-488d-ae95-b4e9f0e49de4) - [Its page on Mindket](https://mindket.com/p/54898cc3-d040-488d-ae95-b4e9f0e49de4)