--- title: "OneJev1.6-0.8B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/7b44bdb4-74c1-4e3b-b0c2-32e28fc4663f" updated: "2026-11-13" --- # OneJev1.6-0.8B > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers typed questions about a screenshot, photo, video or text with a probability for each option. It suits developers who need fast decisions in an agent loop. OneJev1.6-0.8B 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 OneJev1.6-0.8B makes quick decisions from visual or text input. It belongs to the OneJev 1.6 family of multimodal decision models and has 0.8B parameters. It averages the weights of three fine-tunes of Qwen3.5-0.8B. The input can be text or material such as screenshots, photos and video clips, with a few questions you type. It replies with a probability for every option in one pass. ### Who it suits It suits developers building agents that must decide what to do next, check whether a task is done, or judge progress from what is on screen. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Agent developers | Ask what an agent should do next from a screenshot | Choosing between click, type, scroll and stop | | Automation | Workflow builders | Check whether a task has finished | Asking if an invoice has been paid | | Research | Model evaluators | Compare small decision models on a public index | Reading the model's Decision Index score | People who want free-form chat answers can skip it, since it returns probabilities for options you define. ### Where to use it - A Python 3.12 environment on Linux or macOS, using the card's commands - A local server started with the flide tool - Agent loops that send screenshots and read back probabilities | Use case | Who | What they get back | | --- | --- | --- | | Next action choice | Agent developers | One probability for each action option you list | | Task completion check | Automation builders | A probability that the task is finished | | Progress scoring | Agent developers | An expected progress level on a scale you set | ### How to set it up - Python 3.12 and the uv tool - The flide and torchvision packages - About 1.7 GB of disk for the BF16 weights 1. Create a Python 3.12 virtual environment and activate it. 2. Install flide and torchvision. 3. Start the local server with this model. ```bash uv venv -p 3.12 && source .venv/bin/activate uv pip install flide torchvision flide serve --model OmniJev/OneJev1.6-0.8B ``` ### How to use it In a second terminal, send a JSON request to the local server's systemone endpoint. The request holds a state with a task and a screen image, the media itself, and a set of typed questions. The card shows a curl example that encodes a screenshot as base64. | Tool | What it does | | --- | --- | | noul question | Asks a yes or no question and returns the probability of yes | | choice question | Returns one probability per option you describe | | score question | Returns the expected level on a scale you list | ### Example You send a screenshot with the task of paying an open invoice. You ask whether the invoice is paid, what the agent should do next, and how far along the task is. The server returns the yes probability, one probability per action and an expected progress level. ### For newcomers and experts A decision model does not write sentences. It picks among options you give it and says how sure it is. A first small step is to start the server and send the card's example with a screenshot of your own. Training used between 390,000 and 440,000 questions that came from agent runs, videos, images and public decision datasets. On the maker's benchmark board, Decision Index 0.3, it scores 48.82, the best result among models up to 2.5B parameters. Screenshot, photo, video or text → Typed questions with their options → One forward pass through the model → Probability for every option ### Good to know - License: Apache 2.0. - Cost to run: none named. The steps need no API key or paid service. - Data it reaches: it downloads files from the Hugging Face Hub. Screenshots you send stay on your local server. - Not for: free-form text answers. The model returns probabilities for options you define. - The Decision Index scores are the maker's report, run with the board's own kit. Decision Index public part score | Label | Value | | --- | --- | | OneJev1.6-0.8B | 48.82 points | | OneJev1.5-0.8B | 42.71 points | | vLLM-SR d3-nano | 42.22 points | | Sieve 2B v2 | 37.26 points | | vLLM-SR d3-lite | 36.93 points | | Crivo-150M | 36.39 points | ## README ````markdown # OneJev1.6-0.8B Answers typed questions about a screenshot, photo, video or text with a probability for each option. It suits developers who need fast decisions in an agent loop. 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: OneJev1.6-0.8B makes quick decisions from visual or text input. It belongs to the OneJev 1.6 family of multimodal decision models and has 0.8B parameters. It averages the weights of three fine-tunes of Qwen3.5-0.8B. The input can be text or material such as screenshots, photos and video clips, with a few questions you type. It replies with a probability for every option in one pass. Who it suits: It suits developers building agents that must decide what to do next, check whether a task is done, or judge progress from what is on screen. ```table Industry | Who uses it | How they use it | Example Software | Agent developers | Ask what an agent should do next from a screenshot | Choosing between click, type, scroll and stop Automation | Workflow builders | Check whether a task has finished | Asking if an invoice has been paid Research | Model evaluators | Compare small decision models on a public index | Reading the model's Decision Index score ``` People who want free-form chat answers can skip it, since it returns probabilities for options you define. Where to use it: - A Python 3.12 environment on Linux or macOS, using the card's commands - A local server started with the flide tool - Agent loops that send screenshots and read back probabilities ```table Use case | Who | What they get back Next action choice | Agent developers | One probability for each action option you list Task completion check | Automation builders | A probability that the task is finished Progress scoring | Agent developers | An expected progress level on a scale you set ``` How to set it up: - Python 3.12 and the uv tool - The flide and torchvision packages - About 1.7 GB of disk for the BF16 weights 1. Create a Python 3.12 virtual environment and activate it. 2. Install flide and torchvision. 3. Start the local server with this model. ```bash uv venv -p 3.12 && source .venv/bin/activate uv pip install flide torchvision flide serve --model OmniJev/OneJev1.6-0.8B ``` How to use it: In a second terminal, send a JSON request to the local server's systemone endpoint. The request holds a state with a task and a screen image, the media itself, and a set of typed questions. The card shows a curl example that encodes a screenshot as base64. ```table Tool | What it does noul question | Asks a yes or no question and returns the probability of yes choice question | Returns one probability per option you describe score question | Returns the expected level on a scale you list ``` Example: You send a screenshot with the task of paying an open invoice. You ask whether the invoice is paid, what the agent should do next, and how far along the task is. The server returns the yes probability, one probability per action and an expected progress level. For newcomers and experts: A decision model does not write sentences. It picks among options you give it and says how sure it is. A first small step is to start the server and send the card's example with a screenshot of your own. Training used between 390,000 and 440,000 questions that came from agent runs, videos, images and public decision datasets. On the maker's benchmark board, Decision Index 0.3, it scores 48.82, the best result among models up to 2.5B parameters. ```flow Screenshot, photo, video or text Typed questions with their options One forward pass through the model Probability for every option ``` Good to know: - License: Apache 2.0. - Cost to run: none named. The steps need no API key or paid service. - Data it reaches: it downloads files from the Hugging Face Hub. Screenshots you send stay on your local server. - Not for: free-form text answers. The model returns probabilities for options you define. - The Decision Index scores are the maker's report, run with the board's own kit. ```chart Title: Decision Index public part score OneJev1.6-0.8B: 48.82 points OneJev1.5-0.8B: 42.71 points vLLM-SR d3-nano: 42.22 points Sieve 2B v2: 37.26 points vLLM-SR d3-lite: 36.93 points Crivo-150M: 36.39 points ``` ## On Hugging Face, 13 November 2026 - Task: image text to text - 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: 1,885 - Likes: 6 - License: apache-2.0 - Based on: Qwen/Qwen3.5-0.8B - Quantized versions: 2 - Fine-tunes: 2 - 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/OneJev1.6-0.8B - Files: https://huggingface.co/OmniJev/OneJev1.6-0.8B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download OmniJev/OneJev1.6-0.8B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [OmniJev/OneJev1.6-0.8B on Hugging Face](https://huggingface.co/OmniJev/OneJev1.6-0.8B) ## 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 | | --- | --- | --- | --- | --- | --- | | OneJev1.6-0.8B (this page) | 1,885 | 6 | 853M | apache-2.0 | 2026-11-10 | | [OneJev1.6-4B](https://mindket.com/p/54898cc3-d040-488d-ae95-b4e9f0e49de4) | 1,909 | 8 | 4.5B | 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. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · unikcc · 2026-11-09](https://news.ycombinator.com/item?id=50423801): Show HN: Flide: Make Jev-like decision models up to 9.5x faster: Thanks\! Welcome to give it a try. You can check the Decision Index leaderboard at https://huggingface.co/spaces/multimodalart/jev-decision-ind... for reference, and we already support many of the models (see our README). Or you can try our own model, OneJev (https://github.com/OmniJev/OneJev):… ## 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/OneJev1.6-0.8B on Hugging Face](https://huggingface.co/OmniJev/OneJev1.6-0.8B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/7b44bdb4-74c1-4e3b-b0c2-32e28fc4663f) - [Its page on Mindket](https://mindket.com/p/7b44bdb4-74c1-4e3b-b0c2-32e28fc4663f)