--- title: "Laya Vision" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/494ae0ee-63fa-4c77-9daa-e56330226ad5" updated: "2026-11-12" --- # Laya Vision > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers yes/no, multiple-choice and rubric-scored questions about an image in a single pass, with no text generation. It runs a small 201M checkpoint on a CPU and suits vision researchers and developers. Laya Vision 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 thaitea ## About ### What it is Laya Vision answers structured questions about an image. It handles calibrated yes/no questions, multiple-choice questions and rubric-graded scores. Each answer takes one pass through the model and nothing is written out. A small vision-language checkpoint does the work on a CPU. ### Who it suits Vision researchers and developers who want a fast, calibrated check on an image instead of free-form text. It also suits people studying small vision-language models. Who asks structured questions about images | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Vision-language researchers | Test a small model's calibrated answers on images | Ask a yes/no question and read the calibrated result | | Software | Developers | Add a quick image check to a pipeline | Check whether an image meets a yes/no condition | | Content review | Reviewers | Score images against a rubric | Grade a picture with the score mode | | Education | Students | See how a pruned model answers questions | Pick the right option for an image with choice | If you want long written descriptions of an image, you can skip it, since the model does no text generation. ### Where to use it - A web browser: the Space runs on Hugging Face with nothing to install - Your own computer, running Python 3.12 on Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Ask a yes/no question about an image | Developers | A calibrated answer | | Choose between options for an image | Researchers | The option the model picks | | Score an image against a rubric | Reviewers | A rubric-graded score | ### How to set it up - Python 3.12 and pip - The Python packages in requirements.txt 1. Install the Hugging Face hub package and download the Space into a folder. 2. Move into that folder. 3. Install Gradio 6.28.0, then the packages from requirements.txt. 4. Start the app with Python. ```sh pip install -U huggingface_hub hf download thaitea/laya-vision-demo --repo-type space --local-dir laya-vision-demo cd laya-vision-demo pip install gradio==6.28.0 pip install -r requirements.txt python app.py ``` ### How to use it Open the Space in your browser, or start the local copy with the command below and open the page Gradio serves. Give it an image and choose one of three question types, then ask your question. ```sh python app.py ``` | Tool | What it does | | --- | --- | | noul | Answers a calibrated yes/no question about an image | | choice | Answers a multiple-choice question about an image | | score | Grades an image against a rubric | ### Example You give it a photo of a cat and use the yes/no mode to ask whether a cat is in the picture. It returns a calibrated yes or no from a single pass, without writing any text. ### For newcomers and experts If you are new to this, open the Space and try the example photo with the yes/no mode first. Then try the choice and score modes on the same picture. If you know the field, the Space runs thaitea/laya-vision at a pinned revision, a 201M checkpoint made by cutting SmolVLM-256M down to 20 of its 30 language layers. Training drew on The Cauldron, on frames from games and on four datasets with rubric scores. The revision is pinned in app.py, so results stay repeatable. The trade is a small model that answers fast on CPU but cannot write. You give it an image and a question → One forward pass runs on CPU → A calibrated answer or score returns ### Good to know - Hardware: the Space runs on cpu-basic at Hugging Face - Pinned model: the revision is fixed in app.py through LAYA\_REVISION - Example photo: it is credited to Alvesgaspar under CC BY-SA 3.0 - License: cc-by-nc-sa-4 - Cost to run: none named - Not for: writing text, since there is no text generation, and commercial use, since the license is non-commercial ## README ````markdown # Laya Vision Answers yes/no, multiple-choice and rubric-scored questions about an image in a single pass, with no text generation. It runs a small 201M checkpoint on a CPU and suits vision researchers and developers. Made by thaitea 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: Laya Vision answers structured questions about an image. It handles calibrated yes/no questions, multiple-choice questions and rubric-graded scores. Each answer takes one pass through the model and nothing is written out. A small vision-language checkpoint does the work on a CPU. Who it suits: Vision researchers and developers who want a fast, calibrated check on an image instead of free-form text. It also suits people studying small vision-language models. ```table Title: Who asks structured questions about images Industry | Who uses it | How they use it | Example Research | Vision-language researchers | Test a small model's calibrated answers on images | Ask a yes/no question and read the calibrated result Software | Developers | Add a quick image check to a pipeline | Check whether an image meets a yes/no condition Content review | Reviewers | Score images against a rubric | Grade a picture with the score mode Education | Students | See how a pruned model answers questions | Pick the right option for an image with choice ``` If you want long written descriptions of an image, you can skip it, since the model does no text generation. Where to use it: - A web browser: the Space runs on Hugging Face with nothing to install - Your own computer, running Python 3.12 on Windows, macOS or Linux ```table Use case | Who | What they get back Ask a yes/no question about an image | Developers | A calibrated answer Choose between options for an image | Researchers | The option the model picks Score an image against a rubric | Reviewers | A rubric-graded score ``` How to set it up: - Python 3.12 and pip - The Python packages in requirements.txt 1. Install the Hugging Face hub package and download the Space into a folder. 2. Move into that folder. 3. Install Gradio 6.28.0, then the packages from requirements.txt. 4. Start the app with Python. ```sh pip install -U huggingface_hub hf download thaitea/laya-vision-demo --repo-type space --local-dir laya-vision-demo cd laya-vision-demo pip install gradio==6.28.0 pip install -r requirements.txt python app.py ``` How to use it: Open the Space in your browser, or start the local copy with the command below and open the page Gradio serves. Give it an image and choose one of three question types, then ask your question. ```sh python app.py ``` ```table Tool | What it does noul | Answers a calibrated yes/no question about an image choice | Answers a multiple-choice question about an image score | Grades an image against a rubric ``` Example: You give it a photo of a cat and use the yes/no mode to ask whether a cat is in the picture. It returns a calibrated yes or no from a single pass, without writing any text. For newcomers and experts: If you are new to this, open the Space and try the example photo with the yes/no mode first. Then try the choice and score modes on the same picture. If you know the field, the Space runs thaitea/laya-vision at a pinned revision, a 201M checkpoint made by cutting SmolVLM-256M down to 20 of its 30 language layers. Training drew on The Cauldron, on frames from games and on four datasets with rubric scores. The revision is pinned in app.py, so results stay repeatable. The trade is a small model that answers fast on CPU but cannot write. ```flow You give it an image and a question One forward pass runs on CPU A calibrated answer or score returns ``` Good to know: - Hardware: the Space runs on cpu-basic at Hugging Face - Pinned model: the revision is fixed in app.py through LAYA_REVISION - Example photo: it is credited to Alvesgaspar under CC BY-SA 3.0 - License: cc-by-nc-sa-4 - Cost to run: none named - Not for: writing text, since there is no text generation, and commercial use, since the license is non-commercial ## On Hugging Face, 13 November 2026 - Likes: 10 - Built with: gradio - License: cc-by-nc-sa-4.0 ## Links - Space: https://huggingface.co/spaces/thaitea/laya-vision-demo - Files: https://huggingface.co/spaces/thaitea/laya-vision-demo/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, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [thaitea/laya-vision-demo on Hugging Face](https://huggingface.co/spaces/thaitea/laya-vision-demo) ## 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 | | --- | --- | --- | --- | --- | | Laya Vision (this page) | 10 | gradio | cc-by-nc-sa-4.0 | 2026-09-24 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | 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 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · someguy101010 · 2026-09-19](https://news.ycombinator.com/item?id=49767430): Show HN: VisionLaya: Jev with Vision capabilities: This model makes calibrated, typed decisions about an image plus optional text. It answers choice, score and noul (yes/no probability) questions in one forward pass, with no text generation. It adds image input to Laya by replacing Laya's ModernBERT encoder with SmolVLM-256M-Instruct. Laya's… ## 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: 2026-09-24 - First published: 2026-09-19 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open thaitea/laya-vision-demo on Hugging Face](https://huggingface.co/spaces/thaitea/laya-vision-demo) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/494ae0ee-63fa-4c77-9daa-e56330226ad5) - [Its page on Mindket](https://mindket.com/p/494ae0ee-63fa-4c77-9daa-e56330226ad5)