--- title: "Jev-Omni" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1d9d4754-b09e-44ae-b33c-00d5c63c6dc5" updated: "2026-11-13" --- # Jev-Omni > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers a question about text, an image, audio or video by giving a probability for each option you list, instead of writing an explanation. A 12B classifier built on Gemma 4 for developers who need scored decisions. Jev-Omni 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 akhilaaa3 ## About ### What it is Jev-Omni is a decision classifier that works over text, pictures, sound and video. You give it a situation, a question and a list of options. It returns a probability for each option and writes no explanation. It is built on Gemma 4 12B IT and was fine-tuned on 30,000 questions. ### Who it suits Developers who need fast, scored answers to multiple-choice or yes and no questions about mixed media. It fits routing, checking and triage jobs where a probability matters more than prose. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add a scored decision step to a pipeline | Ask whether a meeting has started, given the time | | Media review | Content teams | Classify clips, images or audio against a question | Pick the best-fitting label for a short video | | Operations | Workflow builders | Route items using probabilities rather than text | Choose among a few queues for an incoming item | | Research | Evaluation researchers | Compare decision benchmarks | Test it on DecisionBench or JevBench, which the card reports | You can skip it if you want written answers, chat or explanations. It also lacks a CPU path, since the card says a CUDA GPU is required. ### Where to use it - Python on a machine with a CUDA GPU, with ffmpeg for audio - Pipelines that call a classifier once per question - Research setups that compare decision classifiers | Use case | Who | What they get back | | --- | --- | --- | | Yes or no checks | Developers | A probability for yes and for no | | Multiple choice | Workflow builders | A probability for each of up to 20 options | | Image, audio or video questions | Media teams | Scored answers about a file you attach | ### How to set it up A CUDA GPU is required. The FP32 weights take about 50 GB before runtime overhead, and inference uses BF16 autocast. - Python and a CUDA GPU - The packages from the requirements.txt file in the model repository - ffmpeg, if you plan to send audio 1. Install the packages from the repository's requirements.txt file. 2. Install ffmpeg if you need audio input. 3. Download the repository with huggingface\_hub. 4. Load the classifier with the snippet below. ```bash pip install -U huggingface_hub hf download akhilaaa3/Jev-Omni ``` ### How to use it Download the repository, load the classifier and call predict with a state, a question and your options. The result holds a probability for each option. ```python from huggingface_hub import snapshot_download path = snapshot_download("akhilaaa3/Jev-Omni") import sys; sys.path.insert(0, path) from jev_omni import load_jev_omni classifier = load_jev_omni() result = classifier.predict( state="The meeting starts at 10 AM. It is now 9 AM.", question="Has the meeting started?", options=["Yes", "No"], ) print(result) ``` To ask about an image, audio file or video, add a media path and set modality to image, audio or video. Audio is capped at 30 seconds, and video uses 16 frames. | Tool | What it does | | --- | --- | | load\_jev\_omni | Loads the classifier and fetches the original Gemma 4 multimodal parts | | predict | Takes a state, a question and options, and returns a probability per option | | media and modality | Attach an image, audio file or video to the question | | snapshot\_download | Fetches the repository files, including the loader module | ### Example You give it a note saying a meeting begins at 10 and the time now is 9, ask whether the meeting has started, and offer Yes and No. It returns a probability for each, with No scoring high. ### For newcomers and experts Think of it as a quick judge. You describe a situation, ask a question and list the possible answers, and it tells you how likely each one is. Start with the text example and read the printed result. The model is a fine-tune of Gemma 4 12B IT. On a warm H200, medians over 20 requests were 83 ms for about 2k tokens of text and 26 ms for an image. Thirteen seconds of audio took 31 ms, and a 16-frame video took 504 ms. Calibration error on the medium set is 0.0400, and quality is best with 20 options or fewer. Supply a state, a question and options → Attach an image, audio or video if needed → The classifier scores every option → Read the probability for each option Reported scores by benchmark | Label | Value | | --- | --- | | DecisionBench Medium | 87.57% | | JevBench | 86.15% | | MMAU | 63.10% | | MVBench | 53.10% | ### Good to know - The head accepts up to 256 options, but quality above 20 is not established. - The card says it is independent and not affiliated with TypeSafe AI or its Jev model. - Compared models in the card are scored by their developers and may use different protocols. - License: Apache-2.0, following Gemma 4, with dataset rights kept separate. - Cost to run: none named, but a CUDA GPU is required. - Data it reaches: the text, images, audio and video you pass in, plus files downloaded from Hugging Face. - Not for: written explanations, since it returns probabilities only. ## README ````markdown # Jev-Omni Answers a question about text, an image, audio or video by giving a probability for each option you list, instead of writing an explanation. A 12B classifier built on Gemma 4 for developers who need scored decisions. Made by akhilaaa3 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: Jev-Omni is a decision classifier that works over text, pictures, sound and video. You give it a situation, a question and a list of options. It returns a probability for each option and writes no explanation. It is built on Gemma 4 12B IT and was fine-tuned on 30,000 questions. Who it suits: Developers who need fast, scored answers to multiple-choice or yes and no questions about mixed media. It fits routing, checking and triage jobs where a probability matters more than prose. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add a scored decision step to a pipeline | Ask whether a meeting has started, given the time Media review | Content teams | Classify clips, images or audio against a question | Pick the best-fitting label for a short video Operations | Workflow builders | Route items using probabilities rather than text | Choose among a few queues for an incoming item Research | Evaluation researchers | Compare decision benchmarks | Test it on DecisionBench or JevBench, which the card reports ``` You can skip it if you want written answers, chat or explanations. It also lacks a CPU path, since the card says a CUDA GPU is required. Where to use it: - Python on a machine with a CUDA GPU, with ffmpeg for audio - Pipelines that call a classifier once per question - Research setups that compare decision classifiers ```table Use case | Who | What they get back Yes or no checks | Developers | A probability for yes and for no Multiple choice | Workflow builders | A probability for each of up to 20 options Image, audio or video questions | Media teams | Scored answers about a file you attach ``` How to set it up: A CUDA GPU is required. The FP32 weights take about 50 GB before runtime overhead, and inference uses BF16 autocast. - Python and a CUDA GPU - The packages from the requirements.txt file in the model repository - ffmpeg, if you plan to send audio 1. Install the packages from the repository's requirements.txt file. 2. Install ffmpeg if you need audio input. 3. Download the repository with huggingface_hub. 4. Load the classifier with the snippet below. ```bash pip install -U huggingface_hub hf download akhilaaa3/Jev-Omni ``` How to use it: Download the repository, load the classifier and call predict with a state, a question and your options. The result holds a probability for each option. ```python from huggingface_hub import snapshot_download path = snapshot_download("akhilaaa3/Jev-Omni") import sys; sys.path.insert(0, path) from jev_omni import load_jev_omni classifier = load_jev_omni() result = classifier.predict( state="The meeting starts at 10 AM. It is now 9 AM.", question="Has the meeting started?", options=["Yes", "No"], ) print(result) ``` To ask about an image, audio file or video, add a media path and set modality to image, audio or video. Audio is capped at 30 seconds, and video uses 16 frames. ```table Tool | What it does load_jev_omni | Loads the classifier and fetches the original Gemma 4 multimodal parts predict | Takes a state, a question and options, and returns a probability per option media and modality | Attach an image, audio file or video to the question snapshot_download | Fetches the repository files, including the loader module ``` Example: You give it a note saying a meeting begins at 10 and the time now is 9, ask whether the meeting has started, and offer Yes and No. It returns a probability for each, with No scoring high. For newcomers and experts: Think of it as a quick judge. You describe a situation, ask a question and list the possible answers, and it tells you how likely each one is. Start with the text example and read the printed result. The model is a fine-tune of Gemma 4 12B IT. On a warm H200, medians over 20 requests were 83 ms for about 2k tokens of text and 26 ms for an image. Thirteen seconds of audio took 31 ms, and a 16-frame video took 504 ms. Calibration error on the medium set is 0.0400, and quality is best with 20 options or fewer. ```flow Supply a state, a question and options Attach an image, audio or video if needed The classifier scores every option Read the probability for each option ``` ```chart Title: Reported scores by benchmark DecisionBench Medium: 87.57% JevBench: 86.15% MMAU: 63.10% MVBench: 53.10% ``` Good to know: - The head accepts up to 256 options, but quality above 20 is not established. - The card says it is independent and not affiliated with TypeSafe AI or its Jev model. - Compared models in the card are scored by their developers and may use different protocols. - License: Apache-2.0, following Gemma 4, with dataset rights kept separate. - Cost to run: none named, but a CUDA GPU is required. - Data it reaches: the text, images, audio and video you pass in, plus files downloaded from Hugging Face. - Not for: written explanations, since it returns probabilities only. ## On Hugging Face, 13 November 2026 - Task: text classification - Library: transformers - Parameters: 12B - Weights: about 23.9 GB in BF16; about 6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 3,744 - Likes: 450 - License: apache-2.0 - Based on: google/gemma-4-12B-it - Quantized versions: 4 - Fine-tunes: 3 - Tags: transformers, safetensors, gemma4_unified, image-text-to-text, text-classification, multimodal, merged, decision-model ## Links - Model: https://huggingface.co/akhilaaa3/Jev-Omni - Files: https://huggingface.co/akhilaaa3/Jev-Omni/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download akhilaaa3/Jev-Omni ``` 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 [akhilaaa3/Jev-Omni on Hugging Face](https://huggingface.co/akhilaaa3/Jev-Omni) ## 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 | | --- | --- | --- | --- | --- | --- | | Jev-Omni (this page) | 3,744 | 450 | 12B | apache-2.0 | 2026-10-13 | | [Copernicus (31B)](https://mindket.com/p/edb0cfa0-f450-4b20-ae01-d47bb59546e3) | 75 | 3 | 31.3B | apache-2.0 | 2026-11-11 | | [pplx-decider-flash-v1.2](https://mindket.com/p/ba207a37-74ec-4e43-aa10-b746d0ba8d5a) | 7,052 | 78 | 4.5B | apache-2.0 | 2026-10-19 | | [pplx-decider-v1.2](https://mindket.com/p/88a31af1-401b-4379-82f4-56dcfad230fc) | 5,700 | 74 | 26.1B | apache-2.0 | 2026-10-19 | | [Seb-9B-v2](https://mindket.com/p/fb7adb4a-f993-4988-8461-04d33be95e03) | 265 | 4 | 9.7B | apache-2.0 | 2026-11-10 | | [gemma-4-12B-it](https://mindket.com/p/6830e0b3-1eaa-4197-9fce-f73b57ec505f) | 1,738,905 | 1,735 | 12B | 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. - [Bluesky · Gus · 2026-10-17](https://bsky.app/profile/gusthema.bsky.social/post/3my2wsnistc23): Really neat local vision loop on a MacBook\! 🦖💻⚡ A 12B decision model fine-tuned from Gemma 4 ("Jev-Omni") plays Chrome Dino in real time: 100ms screen slices, 29 new cached tokens/frame, and 10 decisions/sec locally. Weights: https://huggingface.co/akhilaaa3/Jev-Omni ## 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: 2026-10-13 - First published: 2026-09-20 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open akhilaaa3/Jev-Omni on Hugging Face](https://huggingface.co/akhilaaa3/Jev-Omni) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1d9d4754-b09e-44ae-b33c-00d5c63c6dc5) - [Its page on Mindket](https://mindket.com/p/1d9d4754-b09e-44ae-b33c-00d5c63c6dc5)