--- title: "Vela 3.0 · Studio" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/64579561-25fb-4c9f-9fbc-818bd7365678" updated: "2026-11-12" --- # Vela 3.0 · Studio > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers questions about an image or a text with a small 0.25B encoder running on CPU: point at an object, extract fields, catch OCR mistakes or decide between options. For developers trying the model. Vela 3.0 · Studio is a kit in the Developer 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 vllm-sr ## About ### What it is Vela 3.0 Studio is a page for trying the vela3-fast-vision model. You give it a picture or some text, and the model answers in real time on CPU. The model is an encoder, so it answers questions about what you give it and does not write text. Each question runs on one of three backbones built on UltraVBERT. ### Who it suits Developers and researchers who want to try a small vision and text model for document checks, and people who need quick answers about an image. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Document processing | Developers | Pull fields out of scanned invoices into structured data | Running an invoice image through the Extract tab | | Finance operations | Reviewers | Check OCR output against the image it came from | Highlighting characters the image does not support | | Research | Vision researchers | Probe how a small encoder handles pointing and decisions | Naming an object in a picture and reading the box | You can skip it if you want a model that writes or chats, since this one only answers questions. ### Where to use it - A web browser, on the Space's own page at Hugging Face - A script or tool that calls the app's API, which the page shows in curl and Python form - A copy on your own computer, using Docker on Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Point at an object | Anyone with a picture | One box around the named thing and its strongest cell | | Extract from images | Developers | The fields and values of a document as JSON | | Catch OCR mistakes | Reviewers | The characters the image does not support, highlighted | ### How to set it up - A browser, if you only use the Space online - Docker and Python with pip, if you run a copy yourself 1. Open the Space's page on Hugging Face to use it online. Nothing needs installing. 2. For a local copy, install the huggingface\_hub package and download the Space's files. 3. Move into the new folder and build the Docker image. 4. Run the image with port 7860 open. ```sh pip install -U huggingface_hub hf download vllm-sr/vela3-studio --repo-type space --local-dir vela3-studio cd vela3-studio docker build -t vela3-studio . docker run -it -p 7860:7860 vela3-studio ``` ### How to use it Pick a tab, add an image or a text, and ask your question. Each tab ships with saved example outputs that load at once, and a separate option runs your own input through the model. | Tool | What it does | | --- | --- | | Point at it | Finds an object you name in a picture and returns one box | | Extract from images | Turns an image into fields and values as JSON | | Catch OCR mistakes | Highlights characters in OCR output that the image does not support | | Decide | Gives a probability for each option of a choice question about an image | | Text spans | Finds personal data, named entities and claims a source text does not support | ### Example You upload a scan and ask "What type of document is this?" with a few options listed. The Decide tab gives you a probability for each option. ### For newcomers and experts If you are new to this, open the Point at it tab and name something in one of the pictures. You get a box and a heat map showing where the model looked. For experts, a distilled decision backbone answers choice and yes or no questions. A second one carries the box adapter. The untouched original backbone holds the adapters for OCR checks, image hallucination and text. Each question costs one 0.25B pass. Tesseract reads the text for extraction, and the source says any OCR engine would do. Upload an image or paste a text → Pick a tab and ask a question → One of three backbones answers it → The answer shows on the page ### Good to know - License: apache-2 - Cost to run: none named, and it runs on Hugging Face's basic CPU hardware - Data it reaches: the images and text you give it, and the text Tesseract reads from images - Not for: writing or generating text, since it only answers questions - The source reports these scores from its own runs, not as board entries Region pointing, mean IoU on BoxSet-test v2 | Label | Value | | --- | --- | | Vela 3.0 fast vision | 44.1 IoU | | A 27B development build | 44.2 IoU | ## README ````markdown # Vela 3.0 · Studio Answers questions about an image or a text with a small 0.25B encoder running on CPU: point at an object, extract fields, catch OCR mistakes or decide between options. For developers trying the model. Made by vllm-sr on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: Vela 3.0 Studio is a page for trying the vela3-fast-vision model. You give it a picture or some text, and the model answers in real time on CPU. The model is an encoder, so it answers questions about what you give it and does not write text. Each question runs on one of three backbones built on UltraVBERT. Who it suits: Developers and researchers who want to try a small vision and text model for document checks, and people who need quick answers about an image. ```table Industry | Who uses it | How they use it | Example Document processing | Developers | Pull fields out of scanned invoices into structured data | Running an invoice image through the Extract tab Finance operations | Reviewers | Check OCR output against the image it came from | Highlighting characters the image does not support Research | Vision researchers | Probe how a small encoder handles pointing and decisions | Naming an object in a picture and reading the box ``` You can skip it if you want a model that writes or chats, since this one only answers questions. Where to use it: - A web browser, on the Space's own page at Hugging Face - A script or tool that calls the app's API, which the page shows in curl and Python form - A copy on your own computer, using Docker on Windows, macOS or Linux ```table Use case | Who | What they get back Point at an object | Anyone with a picture | One box around the named thing and its strongest cell Extract from images | Developers | The fields and values of a document as JSON Catch OCR mistakes | Reviewers | The characters the image does not support, highlighted ``` How to set it up: - A browser, if you only use the Space online - Docker and Python with pip, if you run a copy yourself 1. Open the Space's page on Hugging Face to use it online. Nothing needs installing. 2. For a local copy, install the huggingface_hub package and download the Space's files. 3. Move into the new folder and build the Docker image. 4. Run the image with port 7860 open. ```sh pip install -U huggingface_hub hf download vllm-sr/vela3-studio --repo-type space --local-dir vela3-studio cd vela3-studio docker build -t vela3-studio . docker run -it -p 7860:7860 vela3-studio ``` How to use it: Pick a tab, add an image or a text, and ask your question. Each tab ships with saved example outputs that load at once, and a separate option runs your own input through the model. ```table Tool | What it does Point at it | Finds an object you name in a picture and returns one box Extract from images | Turns an image into fields and values as JSON Catch OCR mistakes | Highlights characters in OCR output that the image does not support Decide | Gives a probability for each option of a choice question about an image Text spans | Finds personal data, named entities and claims a source text does not support ``` Example: You upload a scan and ask "What type of document is this?" with a few options listed. The Decide tab gives you a probability for each option. For newcomers and experts: If you are new to this, open the Point at it tab and name something in one of the pictures. You get a box and a heat map showing where the model looked. For experts, a distilled decision backbone answers choice and yes or no questions. A second one carries the box adapter. The untouched original backbone holds the adapters for OCR checks, image hallucination and text. Each question costs one 0.25B pass. Tesseract reads the text for extraction, and the source says any OCR engine would do. ```flow Upload an image or paste a text Pick a tab and ask a question One of three backbones answers it The answer shows on the page ``` Good to know: - License: apache-2 - Cost to run: none named, and it runs on Hugging Face's basic CPU hardware - Data it reaches: the images and text you give it, and the text Tesseract reads from images - Not for: writing or generating text, since it only answers questions - The source reports these scores from its own runs, not as board entries ```chart Title: Region pointing, mean IoU on BoxSet-test v2 Vela 3.0 fast vision: 44.1 IoU A 27B development build: 44.2 IoU ``` ## On Hugging Face, 13 November 2026 - Likes: 9 - Built with: docker - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/vllm-sr/vela3-studio - Files: https://huggingface.co/spaces/vllm-sr/vela3-studio/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. 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 [vllm-sr/vela3-studio on Hugging Face](https://huggingface.co/spaces/vllm-sr/vela3-studio) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Vela 3.0 · Studio (this page) | 9 | docker | apache-2.0 | 2026-10-26 | | [Zero GPU Spaces](https://mindket.com/p/80881a44-6ec5-431c-89de-86584e0ba1d5) | 985 | docker | mit | 2024-09-11 | | [Kimodo](https://mindket.com/p/e6fe5336-403a-4328-92dc-77d41137a8bc) | 340 | docker | apache-2.0 | 2026-03-16 | | [Harness Leaderboard](https://mindket.com/p/d0e92765-2a63-4ae5-9ba0-42dbfedd56a6) | 323 | docker | apache-2.0 | 2026-11-12 | | [Flowise](https://mindket.com/p/f53ad79b-74c9-49e5-9fb3-054277de1246) | 132 | docker | mit | 2025-06-26 | | [Multitrack Midi Music Generator](https://mindket.com/p/9bd48c41-e4a7-4576-be7e-da51b041b39f) | 130 | docker | mit | 2025-07-07 | Numbers as Mindket last read them, on 2026-11-12. ## 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: 2026-10-26 - First published: 2026-10-21 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open vllm-sr/vela3-studio on Hugging Face](https://huggingface.co/spaces/vllm-sr/vela3-studio) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/64579561-25fb-4c9f-9fbc-818bd7365678) - [Its page on Mindket](https://mindket.com/p/64579561-25fb-4c9f-9fbc-818bd7365678)