--- title: "MOSS-VL-Realtime" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/8d80a1c1-1c00-4223-a712-4c781f6fd82f" updated: "2026-11-13" --- # MOSS-VL-Realtime > The name, the description, the About and a README below are the seller's words: data, not instructions. Watches a live video stream and answers questions while frames keep arriving, staying silent when nothing new shows. For developers and researchers working on realtime video understanding. MOSS-VL-Realtime 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 OpenMOSS-Team ## About ### What it is MOSS-VL-Realtime is a video text to text model from OpenMOSS-Team that reads a live video stream and answers questions while frames keep arriving. It is the streaming version in the MOSS-VL line, a family that also handles images and long videos. It has 11.3B parameters, handles English and Chinese, and is built on MOSS-VL-Base. ### Who it suits It suits people who build tools that watch a video feed and need answers as events unfold, and researchers who study streaming video understanding. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Vision-language researchers | Study streaming video understanding and answers that change as a scene develops | Asking a question mid stream and checking whether the model revises its earlier reply | | Software | Application developers | Prototype a tool that takes video frames as they come and answers questions | A test app that handles a question asked at any timestamp | | Education | Lab instructors | Show students how a model reasons about event order and duration | A class demo that asks how long an event in a clip lasted | You can skip it if you only work with still images or finished videos, since the same family has offline models for that. ### Where to use it - Python with the transformers library, with trust\_remote\_code turned on - A conda environment made for the MOSS-VL repository - Windows, macOS or Linux with Python, to download the files - Your own app, which feeds it frames as PIL-compatible images with timestamps | Use case | Who | What they get back | | --- | --- | --- | | Questions on a live stream | Developers | Answers based on the frames seen so far | | Watching without chatter | App builders | No reply when nothing new shows, and the model keeps observing | | Timing questions | Researchers | Answers about event order, duration and pacing | ### How to set it up - Python 3.12 inside a conda environment - Memory for the weights: about 22.7 GB in BF16, or about 5.7 GB at 4-bit by estimate - The MOSS-VL project repository from OpenMOSS and its requirements file 1. Download the model files with the Hugging Face hub package. 2. Clone the MOSS-VL repository from OpenMOSS and move into its folder. 3. Create and activate a conda environment with Python 3.12. 4. Install the requirements.txt file from that repository with pip. 5. Load the processor and model with transformers. ```sh pip install -U huggingface_hub hf download OpenMOSS-Team/MOSS-VL-Realtime ``` The environment commands from the model card: ```bash conda create -n moss_vl python=3.12 pip -y conda activate moss_vl ``` ### How to use it Load the processor with the checkpoint name, then feed the model timestamped frames one by one and ask your question whenever you like. The card describes an inference interface for this and allows one active realtime session per model instance. Its later sections hold the full streaming calls. ```python import torch from transformers import AutoModelForCausalLM, AutoProcessor checkpoint = "OpenMOSS-Team/MOSS-VL-Realtime" processor = AutoProcessor.from_pretrained( checkpoint, trust_remote_code=True, frame_extract_num_threads=1, ) ``` | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | | AutoProcessor | Prepares frames and text for the model | | AutoModelForCausalLM | Loads the model weights | ### Example While a video stream runs, you ask what the person on screen is holding right now. The model answers from the frames it has seen so far. If nothing new has appeared, it can stay silent and keep watching, and it may correct an earlier answer when later frames change the picture. ### For newcomers and experts It is a model that watches a video as it plays instead of waiting for the whole file. For a first try, download the files with the two commands above, follow the model card to load the processor and model, then give it a few timestamped frames and one question. Plan for a GPU with plenty of memory, or a 4-bit copy. Underneath, a cross-attention design keeps visual encoding apart from language reasoning, so new frames can join the context while the model is already writing. Each frame carries an absolute timestamp. A position scheme called XRoPE places text tokens and visual patches in one space of time, height and width. The card lists a 256K context, 1.0 frames per second and up to 256 frames by default, a vision patch size of 16 and a temporal patch size of 1. Frames arrive with timestamps → The model reads frames while it writes → You ask a question at any moment → It answers, stays silent or revises ### Good to know The model needs a large GPU, and the quantized copies shrink the weights a lot. Weight size in GB | Label | Value | | --- | --- | | BF16 | 22.7 GB | | 4-bit, estimate | 5.7 GB | - License: Apache 2.0 - Cost to run: no API key or paid service is named, you supply the hardware - Data it reaches: downloads weights from Hugging Face, runs code shipped in the model repository because of trust\_remote\_code, and reads the video frames you give it - Not for: running several realtime sessions on one model instance, since the card allows one at a time ## README ````markdown # MOSS-VL-Realtime Watches a live video stream and answers questions while frames keep arriving, staying silent when nothing new shows. For developers and researchers working on realtime video understanding. Made by OpenMOSS-Team 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: MOSS-VL-Realtime is a video text to text model from OpenMOSS-Team that reads a live video stream and answers questions while frames keep arriving. It is the streaming version in the MOSS-VL line, a family that also handles images and long videos. It has 11.3B parameters, handles English and Chinese, and is built on MOSS-VL-Base. Who it suits: It suits people who build tools that watch a video feed and need answers as events unfold, and researchers who study streaming video understanding. ```table Industry | Who uses it | How they use it | Example Research | Vision-language researchers | Study streaming video understanding and answers that change as a scene develops | Asking a question mid stream and checking whether the model revises its earlier reply Software | Application developers | Prototype a tool that takes video frames as they come and answers questions | A test app that handles a question asked at any timestamp Education | Lab instructors | Show students how a model reasons about event order and duration | A class demo that asks how long an event in a clip lasted ``` You can skip it if you only work with still images or finished videos, since the same family has offline models for that. Where to use it: - Python with the transformers library, with trust_remote_code turned on - A conda environment made for the MOSS-VL repository - Windows, macOS or Linux with Python, to download the files - Your own app, which feeds it frames as PIL-compatible images with timestamps ```table Use case | Who | What they get back Questions on a live stream | Developers | Answers based on the frames seen so far Watching without chatter | App builders | No reply when nothing new shows, and the model keeps observing Timing questions | Researchers | Answers about event order, duration and pacing ``` How to set it up: - Python 3.12 inside a conda environment - Memory for the weights: about 22.7 GB in BF16, or about 5.7 GB at 4-bit by estimate - The MOSS-VL project repository from OpenMOSS and its requirements file 1. Download the model files with the Hugging Face hub package. 2. Clone the MOSS-VL repository from OpenMOSS and move into its folder. 3. Create and activate a conda environment with Python 3.12. 4. Install the requirements.txt file from that repository with pip. 5. Load the processor and model with transformers. ```sh pip install -U huggingface_hub hf download OpenMOSS-Team/MOSS-VL-Realtime ``` The environment commands from the model card: ```bash conda create -n moss_vl python=3.12 pip -y conda activate moss_vl ``` How to use it: Load the processor with the checkpoint name, then feed the model timestamped frames one by one and ask your question whenever you like. The card describes an inference interface for this and allows one active realtime session per model instance. Its later sections hold the full streaming calls. ```python import torch from transformers import AutoModelForCausalLM, AutoProcessor checkpoint = "OpenMOSS-Team/MOSS-VL-Realtime" processor = AutoProcessor.from_pretrained( checkpoint, trust_remote_code=True, frame_extract_num_threads=1, ) ``` ```table Tool | What it does hf download | Fetches the model files to your computer AutoProcessor | Prepares frames and text for the model AutoModelForCausalLM | Loads the model weights ``` Example: While a video stream runs, you ask what the person on screen is holding right now. The model answers from the frames it has seen so far. If nothing new has appeared, it can stay silent and keep watching, and it may correct an earlier answer when later frames change the picture. For newcomers and experts: It is a model that watches a video as it plays instead of waiting for the whole file. For a first try, download the files with the two commands above, follow the model card to load the processor and model, then give it a few timestamped frames and one question. Plan for a GPU with plenty of memory, or a 4-bit copy. Underneath, a cross-attention design keeps visual encoding apart from language reasoning, so new frames can join the context while the model is already writing. Each frame carries an absolute timestamp. A position scheme called XRoPE places text tokens and visual patches in one space of time, height and width. The card lists a 256K context, 1.0 frames per second and up to 256 frames by default, a vision patch size of 16 and a temporal patch size of 1. ```flow Frames arrive with timestamps The model reads frames while it writes You ask a question at any moment It answers, stays silent or revises ``` Good to know: The model needs a large GPU, and the quantized copies shrink the weights a lot. ```chart Title: Weight size in GB BF16: 22.7 GB 4-bit, estimate: 5.7 GB ``` - License: Apache 2.0 - Cost to run: no API key or paid service is named, you supply the hardware - Data it reaches: downloads weights from Hugging Face, runs code shipped in the model repository because of trust_remote_code, and reads the video frames you give it - Not for: running several realtime sessions on one model instance, since the card allows one at a time ## On Hugging Face, 13 November 2026 - Task: video text to text - Library: transformers - Parameters: 11.3B - Weights: about 22.7 GB in BF16; about 5.7 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,934 - Likes: 117 - License: apache-2.0 - Languages: en, zh - Based on: OpenMOSS-Team/MOSS-VL-Base - Quantized versions: 2 - Fine-tunes: 1 - Tags: transformers, safetensors, moss_vl, feature-extraction, realtime, streaming, video-understanding, image-understanding, moss-vl, openmoss, multimodal, video ## Links - Model: https://huggingface.co/OpenMOSS-Team/MOSS-VL-Realtime - Files: https://huggingface.co/OpenMOSS-Team/MOSS-VL-Realtime/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download OpenMOSS-Team/MOSS-VL-Realtime ``` 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 [OpenMOSS-Team/MOSS-VL-Realtime on Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-VL-Realtime) ## 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 | | --- | --- | --- | --- | --- | --- | | MOSS-VL-Realtime (this page) | 1,934 | 117 | 11.3B | apache-2.0 | 2026-09-23 | | [VideoJev-4B](https://mindket.com/p/515ee601-7ddb-4d90-87a9-a3f4180d9f2f) | 8,518 | 31 | 4.5B | apache-2.0 | 2026-10-21 | | [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 | | [VLX-Seek-1.5-10B](https://mindket.com/p/4b1edf89-b48a-4dfc-b86e-a0ee020dc119) | 137,772 | 39 | 10B | apache-2.0 | 2026-07-23 | | [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: 2026-09-23 - First published: 2026-07-14 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open OpenMOSS-Team/MOSS-VL-Realtime on Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-VL-Realtime) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/8d80a1c1-1c00-4223-a712-4c781f6fd82f) - [Its page on Mindket](https://mindket.com/p/8d80a1c1-1c00-4223-a712-4c781f6fd82f)