--- title: "WeVisOne-VL-2B by tencent" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/9aa42bb9-4e2b-4689-bc45-2eaa0d7798c4" updated: "2026-11-12" --- # WeVisOne-VL-2B by tencent > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads an image with a text query and returns object boxes, points, counts, text positions and pixel masks, including alpha mattes. Suits researchers running a 2.4B model on their own computer. WeVisOne-VL-2B by tencent is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by tencent ## About ### What it is WeVisOne-VL-2B is a vision-language model from the WeChat Vision team at Tencent. You give it an image and a text query, and it answers with object boxes, points, counts, text positions, segmentation masks or alpha mattes. It builds on Qwen3-VL and adds a small pixel decoder of 9.6M parameters for the mask work. ### Who it suits It suits researchers and engineers who work on visual grounding, counting, text localization, segmentation or image matting and want one model for all of them. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Computer vision research | Researchers | Test grounding, pointing and counting on their own image sets | Checking which box the model draws for a phrase | | Photo and design tools | Developers | Cut a named subject out of a picture with an alpha matte | Separating a person from the background | | Document processing | Engineers | Find where text sits in an image | Marking the region of a printed line | | Data labeling | Annotation teams | Draft boxes and masks that a person then reviews | Pre-marking objects before manual checks | You can skip it if you need chat answers, video analysis or a way to identify people, since it handles single images and is not meant for that. ### Where to use it - Python scripts that load the model through its inference package - Windows, macOS or Linux with Python, for downloading the files - Any Python environment where you can run the quick start script on an image | Use case | Who | What they get back | | --- | --- | --- | | Referring segmentation | Researchers | A mask for the object a phrase describes | | Alpha matting | Developers | A continuous alpha matte at full resolution | | Counting and pointing | Analysts | Counts and points for the objects you name | | Text localization | Engineers | Boxes that show where text sits in an image | ### How to set it up You need Python and room for roughly 4.9 GB of weights in BF16. - Python on Windows, macOS or Linux - The huggingface\_hub package - The wevisone\_vl inference package from the project's GitHub page 1. Install huggingface\_hub. 2. Download the model files to your computer. 3. Install the wevisone\_vl inference package from the project's GitHub repository, following its own instructions. ```sh pip install -U huggingface_hub hf download tencent/WeVisOne-VL-2B ``` ### How to use it Load the model, open an image, then call predict with a task and a query. The script below asks for a matte of a person and prints the response text and the number of masks returned. ```python from PIL import Image from wevisone_vl import WeVisOneVL model = WeVisOneVL("tencent/WeVisOne-VL-2B", device="auto", dtype="bfloat16") image = Image.open("example.jpg").convert("RGB") result = model.predict(image, task="matting", query="person", temperature=0.0) print(result.response, len(result.masks)) ``` | Tool | What it does | | --- | --- | | WeVisOneVL | Loads the model on a device with a chosen data type | | predict | Runs a task on an image for a query and returns a response with masks | ### Example You point the script at a photo called example.jpg and ask for a matte of a person. The call returns the model's text response and a list of masks, and the script prints the text and how many masks came back. ### For newcomers and experts If you are new to this, think of the model as a helper that finds things in a picture and outlines them. A first small try is the script above with a photo of one person, then reading how many masks it prints. Under the hood, positions of boxes and points are written as dedicated coordinate tokens, and the model returns None when the target is absent. After each box in dense tasks it emits an instance token, and a 9.6M-parameter decoder turns that state and the box into masks. The trade-off is no step-by-step reasoning and single-turn image input only. Image and text query go in → Qwen3-VL backbone writes boxes and points → Instance token marks each predicted box → Pixel decoder builds masks and alpha mattes → You read the response and masks ### Good to know The weights take about 4.9 GB in BF16, and about 1.2 GB at 4-bit as an estimate. - Limits: fine alpha edges are sensitive to image resolution, heavy occlusion, complex transparency and how the prompt is worded - License: Apache 2.0 for the Tencent-written code, and third-party parts keep their own licenses - Cost to run: none named - Data it reaches: the images you pass in and the downloaded model files - Not for: autonomous high-impact decisions, identifying people, or video input ## README ````markdown # WeVisOne-VL-2B by tencent Reads an image with a text query and returns object boxes, points, counts, text positions and pixel masks, including alpha mattes. Suits researchers running a 2.4B model on their own computer. Made by tencent 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: WeVisOne-VL-2B is a vision-language model from the WeChat Vision team at Tencent. You give it an image and a text query, and it answers with object boxes, points, counts, text positions, segmentation masks or alpha mattes. It builds on Qwen3-VL and adds a small pixel decoder of 9.6M parameters for the mask work. Who it suits: It suits researchers and engineers who work on visual grounding, counting, text localization, segmentation or image matting and want one model for all of them. ```table Industry | Who uses it | How they use it | Example Computer vision research | Researchers | Test grounding, pointing and counting on their own image sets | Checking which box the model draws for a phrase Photo and design tools | Developers | Cut a named subject out of a picture with an alpha matte | Separating a person from the background Document processing | Engineers | Find where text sits in an image | Marking the region of a printed line Data labeling | Annotation teams | Draft boxes and masks that a person then reviews | Pre-marking objects before manual checks ``` You can skip it if you need chat answers, video analysis or a way to identify people, since it handles single images and is not meant for that. Where to use it: - Python scripts that load the model through its inference package - Windows, macOS or Linux with Python, for downloading the files - Any Python environment where you can run the quick start script on an image ```table Use case | Who | What they get back Referring segmentation | Researchers | A mask for the object a phrase describes Alpha matting | Developers | A continuous alpha matte at full resolution Counting and pointing | Analysts | Counts and points for the objects you name Text localization | Engineers | Boxes that show where text sits in an image ``` How to set it up: You need Python and room for roughly 4.9 GB of weights in BF16. - Python on Windows, macOS or Linux - The huggingface_hub package - The wevisone_vl inference package from the project's GitHub page 1. Install huggingface_hub. 2. Download the model files to your computer. 3. Install the wevisone_vl inference package from the project's GitHub repository, following its own instructions. ```sh pip install -U huggingface_hub hf download tencent/WeVisOne-VL-2B ``` How to use it: Load the model, open an image, then call predict with a task and a query. The script below asks for a matte of a person and prints the response text and the number of masks returned. ```python from PIL import Image from wevisone_vl import WeVisOneVL model = WeVisOneVL("tencent/WeVisOne-VL-2B", device="auto", dtype="bfloat16") image = Image.open("example.jpg").convert("RGB") result = model.predict(image, task="matting", query="person", temperature=0.0) print(result.response, len(result.masks)) ``` ```table Tool | What it does WeVisOneVL | Loads the model on a device with a chosen data type predict | Runs a task on an image for a query and returns a response with masks ``` Example: You point the script at a photo called example.jpg and ask for a matte of a person. The call returns the model's text response and a list of masks, and the script prints the text and how many masks came back. For newcomers and experts: If you are new to this, think of the model as a helper that finds things in a picture and outlines them. A first small try is the script above with a photo of one person, then reading how many masks it prints. Under the hood, positions of boxes and points are written as dedicated coordinate tokens, and the model returns None when the target is absent. After each box in dense tasks it emits an instance token, and a 9.6M-parameter decoder turns that state and the box into masks. The trade-off is no step-by-step reasoning and single-turn image input only. ```flow Image and text query go in Qwen3-VL backbone writes boxes and points Instance token marks each predicted box Pixel decoder builds masks and alpha mattes You read the response and masks ``` Good to know: The weights take about 4.9 GB in BF16, and about 1.2 GB at 4-bit as an estimate. - Limits: fine alpha edges are sensitive to image resolution, heavy occlusion, complex transparency and how the prompt is worded - License: Apache 2.0 for the Tencent-written code, and third-party parts keep their own licenses - Cost to run: none named - Data it reaches: the images you pass in and the downloaded model files - Not for: autonomous high-impact decisions, identifying people, or video input ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 2.4B - Weights: about 4.9 GB in BF16; about 1.2 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 15,085 - Likes: 85 - License: apache-2.0 - Quantized versions: 6 - Fine-tunes: 2 - Tags: transformers, safetensors, qwen3_vl, image-text-to-text, vision-language, object-detection, referring-expression-comprehension, object-counting, point-grounding, referring-expression-segmentation, image-matting, conversational ## Links - Model: https://huggingface.co/tencent/WeVisOne-VL-2B - Files: https://huggingface.co/tencent/WeVisOne-VL-2B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download tencent/WeVisOne-VL-2B ``` 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 [tencent/WeVisOne-VL-2B on Hugging Face](https://huggingface.co/tencent/WeVisOne-VL-2B) ## 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 | | --- | --- | --- | --- | --- | --- | | WeVisOne-VL-2B by tencent (this page) | 15,085 | 85 | 2.4B | apache-2.0 | 2026-10-28 | | [WeVisOne-VL-4B](https://mindket.com/p/6d8cd6a2-35fb-4d9b-a126-1f4e12e81746) | 18,908 | 122 | 4.8B | apache-2.0 | 2026-10-27 | | [VLX-Seek-1.5-10B](https://mindket.com/p/4b1edf89-b48a-4dfc-b86e-a0ee020dc119) | 137,772 | 39 | 10B | apache-2.0 | 2026-07-23 | | [NV-Reason-CT](https://mindket.com/p/f1bfdffe-c945-4dc6-8e3f-e61105a05537) | 5,524 | 94 | 5.3B | openmdw-1.1 | 2026-09-25 | | [Sage-A1.5](https://mindket.com/p/8f410da0-850f-4ea5-bda9-65555f4144cd) | 22,224 | 404 | 36B | apache-2.0 | 2026-10-22 | | [Qwen-Image-Bench](https://mindket.com/p/3ae32930-3429-4622-acd9-87abf1987c6e) | 20,875 | 116 | 27.4B | apache-2.0 | 2026-05-28 | 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 · deployedbyai · 2026-10-24](https://bsky.app/profile/deployedbyai.bsky.social/post/3n43pykivs2ck): Tencent: WeVisOne-VL-4B and WeVisOne-VL-2B on Hugging Face, 9.6M pixel decoder - [Bluesky · @aichina.news · 2026-10-29](https://bsky.app/profile/aichina.news/post/3myzmcyaxni2t): Unified detection, segmentation and alpha matting through one 9.6M pixel decoder, built on Qwen3-VL and Apache-2.0 — the scope is unusual for a 2B/4B pair. Fine alpha edges reportedly buckle under occlusion, so hard to see this replacing a dedicated matting stack in production yet. - [Bluesky · deployedbyai · 2026-10-24](https://bsky.app/profile/deployedbyai.bsky.social/post/3n4fn35d4a3uz): The source, dated, on The Deploy Log: https://deployedbyai.com/log/dc5fd912415f/wevisone-vl-4b-and-wevisone-vl-2b/?k=br ## 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: 2026-10-28 - First published: 2026-10-22 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open tencent/WeVisOne-VL-2B on Hugging Face](https://huggingface.co/tencent/WeVisOne-VL-2B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9aa42bb9-4e2b-4689-bc45-2eaa0d7798c4) - [Its page on Mindket](https://mindket.com/p/9aa42bb9-4e2b-4689-bc45-2eaa0d7798c4)