--- title: "VideoJev-4B" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/515ee601-7ddb-4d90-87a9-a3f4180d9f2f" updated: "2026-11-13" --- # VideoJev-4B > The name, the description, the About and a README below are the seller's words: data, not instructions. Finds when an event happens in a video and answers multiple-choice questions about it, by scoring candidates directly. Built on Qwen3.5-4B for video researchers and developers. VideoJev-4B is a kit in the Research 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 ZhuYuhan ## About ### What it is VideoJev-4B is a video understanding model with 4.5 billion parameters, built on Qwen3.5-4B. It is a video System One model, and it scores candidates directly instead of writing free text. It does two jobs: finding the time span in a clip that matches a sentence, and picking the right answer to a multiple-choice question about the clip. ### Who it suits It suits video researchers and developers who want to locate moments in clips or answer choice questions about them with a compact model. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Video editors and archivists | Find the moment in a clip that matches a sentence | Locate a woman in green eating pasta | | Research | Video understanding researchers | Test temporal grounding and multiple-choice video QA | Compare answers across clips | | Software | App developers | Add moment search and clip questions to a tool | Answer a choice question about an uploaded video | People who need free-form written answers or video generation can skip it, since it scores time spans and answer options. ### Where to use it - Python with the transformers library on an NVIDIA GPU, as in the card's example - The card's tested setup: Python 3.12, PyTorch 2.8 with CUDA 12.8, and an H200 - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Temporal grounding | Researchers | A time interval in seconds plus a probability for each second | | Multiple-choice video QA | Developers | The chosen option, its index and a probability for each option | | Benchmark runs | Evaluators | Scores for both tasks from one model | ### How to set it up You need a few things first. - Python 3.12 and a matching PyTorch build, installed first - An NVIDIA GPU, since the example loads the model on cuda:0 - Disk space for about 8.9 GB of BF16 weights 1. Install a PyTorch build that matches your CUDA version. 2. Install the Python packages the card lists. 3. Build flash-attn and causal-conv1d without build isolation. 4. Run the example script. ```sh pip install -U huggingface_hub hf download ZhuYuhan/VideoJev-4B ``` ```bash pip install transformers==5.14.1 accelerate safetensors numpy pillow av==18.1.0 \ flash-linear-attention==0.4.2 fla-core==0.4.2 pip install flash-attn causal-conv1d --no-build-isolation ``` ### How to use it You load the processor and model, prepare the model for inference, then pass a video and a list of questions to predict\_video. Each question is either a grounding query or a choice query with options. ```python import json import torch from huggingface_hub import hf_hub_download from transformers import AutoModel, AutoProcessor video = hf_hub_download( repo_id="ZhuYuhan/VideoJev-Demo", filename="eating_spaghetti.mp4", repo_type="dataset", revision="7e2b9685bca592c3b929268f6fcf82c4ad74b267", ) repo = "ZhuYuhan/VideoJev-4B" processor = AutoProcessor.from_pretrained(repo, trust_remote_code=True) model = AutoModel.from_pretrained( repo, trust_remote_code=True, dtype=torch.bfloat16, device_map={"": "cuda:0"}, attn_implementation="flash_attention_2", ) model.prepare_for_inference() questions = [ {"id": "grounding-1", "task": "grounding", "query": "The woman in green eats pasta."}, {"id": "qa-1", "task": "choice", "query": "What is the woman in green eating?", "options": ["A. Pasta.", "B. Rice.", "C. Soup."]}, ] results = model.predict_video( processor, video, questions, fps=2.0, max_frames=2048, context=8192, ) print(json.dumps(results, indent=2)) ``` | Tool | What it does | | --- | --- | | AutoProcessor | Prepares the video and questions for the model | | AutoModel | Loads VideoJev-4B, with trust\_remote\_code turned on | | prepare\_for\_inference | Gets the loaded model ready to score videos | | predict\_video | Scores a list of grounding and choice questions against a video | | threshold | The grounding cut-off, 0.25 by default and adjustable per call | ### Example You run the card's script on a 10-second clip of someone eating spaghetti. The grounding query about a woman in green eating pasta returns an interval from 3.0 to 10.0 seconds. The choice question about what she eats returns the first option, pasta, with a probability above 0.99. ### For newcomers and experts If you are new, think of this as a model that watches a clip and either marks when something happens or picks the right answer from a list. Install the packages, run the card's script as written and read the printed results. Underneath, grounding gives each moment an independent sigmoid score, so several spans can be marked, while choice questions use a softmax over the options. The default grounding cut-off is 0.25, and passing 0.4 raises it for one call. The example samples the clip at 2 frames per second with up to 2048 frames and a context of 8192. Video and questions go to the processor → Frames are sampled at 2 FPS → Model scores each candidate directly → Sigmoid scores for time, softmax for options → Interval or answer is returned ### Good to know - License: Apache 2.0. - Cost to run: no API key or paid service named. You need an NVIDIA GPU and enough memory for about 8.9 GB of weights. - Data it reaches: the video files you pass in. The example downloads a demo clip from Hugging Face, and trust\_remote\_code runs code from the repository. - Not for: free-form written answers, or machines without a CUDA GPU, since the card's example targets one. ## README ````markdown # VideoJev-4B Finds when an event happens in a video and answers multiple-choice questions about it, by scoring candidates directly. Built on Qwen3.5-4B for video researchers and developers. Made by ZhuYuhan 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: VideoJev-4B is a video understanding model with 4.5 billion parameters, built on Qwen3.5-4B. It is a video System One model, and it scores candidates directly instead of writing free text. It does two jobs: finding the time span in a clip that matches a sentence, and picking the right answer to a multiple-choice question about the clip. Who it suits: It suits video researchers and developers who want to locate moments in clips or answer choice questions about them with a compact model. ```table Industry | Who uses it | How they use it | Example Media | Video editors and archivists | Find the moment in a clip that matches a sentence | Locate a woman in green eating pasta Research | Video understanding researchers | Test temporal grounding and multiple-choice video QA | Compare answers across clips Software | App developers | Add moment search and clip questions to a tool | Answer a choice question about an uploaded video ``` People who need free-form written answers or video generation can skip it, since it scores time spans and answer options. Where to use it: - Python with the transformers library on an NVIDIA GPU, as in the card's example - The card's tested setup: Python 3.12, PyTorch 2.8 with CUDA 12.8, and an H200 - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Temporal grounding | Researchers | A time interval in seconds plus a probability for each second Multiple-choice video QA | Developers | The chosen option, its index and a probability for each option Benchmark runs | Evaluators | Scores for both tasks from one model ``` How to set it up: You need a few things first. - Python 3.12 and a matching PyTorch build, installed first - An NVIDIA GPU, since the example loads the model on cuda:0 - Disk space for about 8.9 GB of BF16 weights 1. Install a PyTorch build that matches your CUDA version. 2. Install the Python packages the card lists. 3. Build flash-attn and causal-conv1d without build isolation. 4. Run the example script. ```sh pip install -U huggingface_hub hf download ZhuYuhan/VideoJev-4B ``` ```bash pip install transformers==5.14.1 accelerate safetensors numpy pillow av==18.1.0 \ flash-linear-attention==0.4.2 fla-core==0.4.2 pip install flash-attn causal-conv1d --no-build-isolation ``` How to use it: You load the processor and model, prepare the model for inference, then pass a video and a list of questions to predict_video. Each question is either a grounding query or a choice query with options. ```python import json import torch from huggingface_hub import hf_hub_download from transformers import AutoModel, AutoProcessor video = hf_hub_download( repo_id="ZhuYuhan/VideoJev-Demo", filename="eating_spaghetti.mp4", repo_type="dataset", revision="7e2b9685bca592c3b929268f6fcf82c4ad74b267", ) repo = "ZhuYuhan/VideoJev-4B" processor = AutoProcessor.from_pretrained(repo, trust_remote_code=True) model = AutoModel.from_pretrained( repo, trust_remote_code=True, dtype=torch.bfloat16, device_map={"": "cuda:0"}, attn_implementation="flash_attention_2", ) model.prepare_for_inference() questions = [ {"id": "grounding-1", "task": "grounding", "query": "The woman in green eats pasta."}, {"id": "qa-1", "task": "choice", "query": "What is the woman in green eating?", "options": ["A. Pasta.", "B. Rice.", "C. Soup."]}, ] results = model.predict_video( processor, video, questions, fps=2.0, max_frames=2048, context=8192, ) print(json.dumps(results, indent=2)) ``` ```table Tool | What it does AutoProcessor | Prepares the video and questions for the model AutoModel | Loads VideoJev-4B, with trust_remote_code turned on prepare_for_inference | Gets the loaded model ready to score videos predict_video | Scores a list of grounding and choice questions against a video threshold | The grounding cut-off, 0.25 by default and adjustable per call ``` Example: You run the card's script on a 10-second clip of someone eating spaghetti. The grounding query about a woman in green eating pasta returns an interval from 3.0 to 10.0 seconds. The choice question about what she eats returns the first option, pasta, with a probability above 0.99. For newcomers and experts: If you are new, think of this as a model that watches a clip and either marks when something happens or picks the right answer from a list. Install the packages, run the card's script as written and read the printed results. Underneath, grounding gives each moment an independent sigmoid score, so several spans can be marked, while choice questions use a softmax over the options. The default grounding cut-off is 0.25, and passing 0.4 raises it for one call. The example samples the clip at 2 frames per second with up to 2048 frames and a context of 8192. ```flow Video and questions go to the processor Frames are sampled at 2 FPS Model scores each candidate directly Sigmoid scores for time, softmax for options Interval or answer is returned ``` Good to know: - License: Apache 2.0. - Cost to run: no API key or paid service named. You need an NVIDIA GPU and enough memory for about 8.9 GB of weights. - Data it reaches: the video files you pass in. The example downloads a demo clip from Hugging Face, and trust_remote_code runs code from the repository. - Not for: free-form written answers, or machines without a CUDA GPU, since the card's example targets one. ## On Hugging Face, 13 November 2026 - Task: feature extraction - Library: transformers - Parameters: 4.5B - Weights: about 8.9 GB in BF16; about 2.2 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 8,518 - Likes: 31 - License: apache-2.0 - Based on: Qwen/Qwen3.5-4B - Quantized versions: 1 - Tags: transformers, safetensors, videojev, feature-extraction, video, video-question-answering, temporal-grounding, decision-model ## Links - Model: https://huggingface.co/ZhuYuhan/VideoJev-4B - Files: https://huggingface.co/ZhuYuhan/VideoJev-4B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ZhuYuhan/VideoJev-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 [ZhuYuhan/VideoJev-4B on Hugging Face](https://huggingface.co/ZhuYuhan/VideoJev-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 | | --- | --- | --- | --- | --- | --- | | VideoJev-4B (this page) | 8,518 | 31 | 4.5B | apache-2.0 | 2026-10-21 | | [MOSS-VL-Realtime](https://mindket.com/p/8d80a1c1-1c00-4223-a712-4c781f6fd82f) | 1,934 | 117 | 11.3B | apache-2.0 | 2026-09-23 | | [LeVJEPA 1.5 Large](https://mindket.com/p/5f56ef1c-8d34-449c-af8e-1d2b293ccd55) | 269 | 18 | 303M | cc-by-nc-4.0 | 2026-11-05 | | [MANTA-27B](https://mindket.com/p/407608cf-6b3c-48f6-8e9d-74c777d9ad28) | 46 | 4 | 27.5B | apache-2.0 | 2026-11-10 | | [EarthJev](https://mindket.com/p/d973b9a2-651e-4bec-8aa3-966815476618) | 159 | 2 | 1.1B | apache-2.0 | 2026-11-03 | | [MERT-v2-FullSong](https://mindket.com/p/7ea6f7f0-baca-4fd5-8224-87db01543a6f) | 13,790 | 57 | 632M | cc-by-nc-4.0 | 2026-09-29 | Numbers as Mindket last read them, on 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: 2026-10-21 - First published: 2026-10-10 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ZhuYuhan/VideoJev-4B on Hugging Face](https://huggingface.co/ZhuYuhan/VideoJev-4B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/515ee601-7ddb-4d90-87a9-a3f4180d9f2f) - [Its page on Mindket](https://mindket.com/p/515ee601-7ddb-4d90-87a9-a3f4180d9f2f)