--- title: "VIDRAFT" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b46d1f60-3346-43c9-9491-629c5de74700" updated: "2026-11-12" --- # VIDRAFT > The name, the description, the About and a README below are the seller's words: data, not instructions. The home page of VIDRAFT, a Seoul-based AI model lab, as a static site. It presents its open models and tools for training, merging, compressing, testing and speeding up language models. VIDRAFT 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 VIDraft ## About ### What it is This Space is the home page of VIDRAFT, an AI company in Seoul. It publishes open language models and tools for checking them. The page is a static site, with pages, images, a paper PDF and a Darwin model list in the repository. The page shows four ways it makes models: pretraining from scratch, merging, cross-architecture breeding and compression. ### Who it suits Machine learning researchers and engineers who follow open model labs and want one overview of what VIDRAFT offers. People who want to run large models on a PC or phone can look at the compression line. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI research | Model researchers | Read about the merging, pretraining and verification approaches | Looking at how the Darwin family is described | | On-device AI | Developers running models on PCs and phones | Look at the compression line for local use | Checking which model sizes POCKET covers | | Security | Testers of code and AI agents | Look at the proof-based testing tool | Reading what Chitos is meant to test | | Data and databases | Analysts | Look at the natural language to SQL tool | Reading how ViSQL checks queries by running them | If you want a tool to run right now, you can skip it, because the page is an overview of a lab. ### Where to use it - A web browser, on the Space's page at Hugging Face - Windows, macOS or Linux, by opening a downloaded index.html in any browser | Use case | Who | What they get back | | --- | --- | --- | | Survey a model lab | Researchers | A table of model lines and tools with what each one does | | Find the named models and datasets | Engineers | Six model names and six dataset names to look up on Hugging Face | ### How to set it up Online use needs nothing, since the page runs in the browser. To keep a copy, you need little. - Python and pip, only to download the files with the Hugging Face tool - A web browser to open the page 1. Install the Hugging Face hub package. 2. Download the Space's files into a local folder. 3. Move into that folder. 4. Open index.html in a web browser. ```sh pip install -U huggingface_hub hf download VIDraft/AI --repo-type space --local-dir AI cd AI ``` ### How to use it Open the page and read through the product lines. The table below lists the main ones as the page describes them. | Tool | What it does | | --- | --- | | Darwin | Merges models by evolution without any training | | AETHER | A foundation model pretrained from scratch with a mix of 5, 7 and 11 attention types, with open weights, code and logs under Apache 2.0 | | POCKET | Runs models from 26B to 180B on a PC, laptop or phone through stock llama.cpp, with no GPU needed | | ZTC | Judges how far an answer can be trusted without generating any tokens | | VKAE, VKUE and VKIE | Speed up inference on a GPU, a CPU and an NPU | | NPU (VKORE) | Runs inference, LoRA tuning and pretraining on NPUs made only for inference | | AX-RAY | Diagnoses an LLM for safety, causality and trust before deployment | | Chitos | Security tests for code and AI agents that rest on proof | | ViSQL | Turns questions into SQL and checks the result by running it | | CellOS | Cell simulation that can be differentiated, plus research agents that correct themselves | ### Example You open the page and read the POCKET entry, which says big models can run on an ordinary PC, laptop or phone through llama.cpp, with no graphics card. You then look up the model names the Space lists on Hugging Face. ### For newcomers and experts If you are new to this, open the Space's page and read the opening paragraph and the product table. Nothing needs installing, and you get a map of what this lab works on. If you know the field, the page reports benchmark and leaderboard results for the Darwin family and others. These are the lab's own claims, and the page also links the project to a paper PDF, so check them against the original leaderboards. The page itself is plain static files, with models and datasets named for you to fetch on Hugging Face. Open the page in a browser → Read the lab's four ways of making models → Browse the product table → Look up the named models and datasets ### Good to know The numbers on the page are the lab's own reports, and the source gives no way to check them here. - License: Apache 2.0, as the Space's license line says - Cost to run: none named for the page itself. Running the models it names would depend on their own terms - Data it reaches: it names six models and six datasets on Hugging Face, and its README mentions the Hugging Face public API for usage figures ## README ````markdown # VIDRAFT The home page of VIDRAFT, a Seoul-based AI model lab, as a static site. It presents its open models and tools for training, merging, compressing, testing and speeding up language models. Made by VIDraft 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: This Space is the home page of VIDRAFT, an AI company in Seoul. It publishes open language models and tools for checking them. The page is a static site, with pages, images, a paper PDF and a Darwin model list in the repository. The page shows four ways it makes models: pretraining from scratch, merging, cross-architecture breeding and compression. Who it suits: Machine learning researchers and engineers who follow open model labs and want one overview of what VIDRAFT offers. People who want to run large models on a PC or phone can look at the compression line. ```table Industry | Who uses it | How they use it | Example AI research | Model researchers | Read about the merging, pretraining and verification approaches | Looking at how the Darwin family is described On-device AI | Developers running models on PCs and phones | Look at the compression line for local use | Checking which model sizes POCKET covers Security | Testers of code and AI agents | Look at the proof-based testing tool | Reading what Chitos is meant to test Data and databases | Analysts | Look at the natural language to SQL tool | Reading how ViSQL checks queries by running them ``` If you want a tool to run right now, you can skip it, because the page is an overview of a lab. Where to use it: - A web browser, on the Space's page at Hugging Face - Windows, macOS or Linux, by opening a downloaded index.html in any browser ```table Use case | Who | What they get back Survey a model lab | Researchers | A table of model lines and tools with what each one does Find the named models and datasets | Engineers | Six model names and six dataset names to look up on Hugging Face ``` How to set it up: Online use needs nothing, since the page runs in the browser. To keep a copy, you need little. - Python and pip, only to download the files with the Hugging Face tool - A web browser to open the page 1. Install the Hugging Face hub package. 2. Download the Space's files into a local folder. 3. Move into that folder. 4. Open index.html in a web browser. ```sh pip install -U huggingface_hub hf download VIDraft/AI --repo-type space --local-dir AI cd AI ``` How to use it: Open the page and read through the product lines. The table below lists the main ones as the page describes them. ```table Tool | What it does Darwin | Merges models by evolution without any training AETHER | A foundation model pretrained from scratch with a mix of 5, 7 and 11 attention types, with open weights, code and logs under Apache 2.0 POCKET | Runs models from 26B to 180B on a PC, laptop or phone through stock llama.cpp, with no GPU needed ZTC | Judges how far an answer can be trusted without generating any tokens VKAE, VKUE and VKIE | Speed up inference on a GPU, a CPU and an NPU NPU (VKORE) | Runs inference, LoRA tuning and pretraining on NPUs made only for inference AX-RAY | Diagnoses an LLM for safety, causality and trust before deployment Chitos | Security tests for code and AI agents that rest on proof ViSQL | Turns questions into SQL and checks the result by running it CellOS | Cell simulation that can be differentiated, plus research agents that correct themselves ``` Example: You open the page and read the POCKET entry, which says big models can run on an ordinary PC, laptop or phone through llama.cpp, with no graphics card. You then look up the model names the Space lists on Hugging Face. For newcomers and experts: If you are new to this, open the Space's page and read the opening paragraph and the product table. Nothing needs installing, and you get a map of what this lab works on. If you know the field, the page reports benchmark and leaderboard results for the Darwin family and others. These are the lab's own claims, and the page also links the project to a paper PDF, so check them against the original leaderboards. The page itself is plain static files, with models and datasets named for you to fetch on Hugging Face. ```flow Open the page in a browser Read the lab's four ways of making models Browse the product table Look up the named models and datasets ``` Good to know: The numbers on the page are the lab's own reports, and the source gives no way to check them here. - License: Apache 2.0, as the Space's license line says - Cost to run: none named for the page itself. Running the models it names would depend on their own terms - Data it reaches: it names six models and six datasets on Hugging Face, and its README mentions the Hugging Face public API for usage figures ## On Hugging Face, 13 November 2026 - Likes: 24 - Built with: static - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/VIDraft/AI - Files: https://huggingface.co/spaces/VIDraft/AI/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 [VIDraft/AI on Hugging Face](https://huggingface.co/spaces/VIDraft/AI) ## 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 | | --- | --- | --- | --- | --- | | VIDRAFT (this page) | 24 | static | apache-2.0 | 2026-11-12 | | [Arena Leaderboard](https://mindket.com/p/613ca8f0-8146-4a48-87d6-43b8ee2ce3b6) | 5,039 | static | apache-2.0 | 2026-02-21 | | [Browser](https://mindket.com/p/d4e4cb47-8f53-4094-9a76-4fd29147a65b) | 533 | static | cc0-1.0 | 2026-10-18 | | [Model Pulse](https://mindket.com/p/017106fd-a3d1-4c20-944b-38f048d8b624) | 214 | static | apache-2.0 | 2026-11-04 | | [MTEB-BR Leaderboard](https://mindket.com/p/05f89589-d44c-4616-8efb-c50c9ba6ad94) | 126 | static | apache-2.0 | 2026-07-29 | | [Hub Stats](https://mindket.com/p/03d71953-8093-47a0-a444-c4f562d3071d) | 65 | static | apache-2.0 | 2026-11-12 | Numbers as Mindket last read them, on 2026-11-12. ## 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 · ctulu82 · 2024-11-28](https://news.ycombinator.com/item?id=42261391): Show HN: AI Hackathon\_ Prize 20K USD '1-Min Creative Innovation with AI': VIDraft's Mouse Hackathon is an innovative event focused on creating and deploying AI-powered web services. Participants can use the Mouse-I tool to automatically generate and deploy functional web services within 60 seconds by simply inputting prompts. This tool combines AI automation with… - [Bluesky · Awesome Agents · 2026-07-26](https://bsky.app/profile/awesomeagents.bsky.social/post/3mrli45izol2p): New podcast episode: A 35B Model That Runs on Your Phone Was Bred, Not Trained VIDRAFT compressed its leaderboard-climbing Darwin-36B-Opus into POCKET-35B, a GPU-free model for phones and CPUs, but its headline GPQA score depends on how you count. Full episode in the reply. \#AI \#Podcast \#LLM - [Hacker News · vik\_bhati · 2026-10-05](https://news.ycombinator.com/item?id=49960215): Ask HN: Who wants to be hired? (October 2026): Location: Blacksburg, VA, USA Remote: Yes, full-time, USA Willing to relocate: Yes Technologies: Python, Java, C++, SQL, PyTorch, TensorFlow, Hugging Face, LLM post-training (SFT, LoRA, GRPO), reinforcement learning, AI agent evaluation, RAG, GraphRAG, MCP, Spring Boot, FastAPI, Google Cloud,… - [Bluesky · Undercode News · 2026-05-15](https://bsky.app/profile/undercodenews.bsky.social/post/3mlugia277u27): Training-Free AI Shockwave: Darwin Family Hits 8889% on GPQA Diamond Without a Single Gradient Step ⚡ Breakthrough Overview: A Model That Learns Without Training The latest research from VIDRAFT introduces a radical shift in how large language models can be improved. Instead of relying on… - [Hacker News · seawolf2357 · 2025-06-18](https://news.ycombinator.com/item?id=44307518): dead: Open GAMMA - AI PPT Generator 'GamJa' Project Introduction Revolutionary AI presentation generator presented by OpenFree AI Community\! Create professional-level PPTs with just a few clicks. Completely FREE\! Create Premium PPTs with Free GAMMA\! DEMO:… - [Hacker News · VIDraft · 2024-11-28](https://news.ycombinator.com/item?id=42261418): Show HN: AI Hackathon\_ Prize 20K USD '1-Min Creative Innovation with AI': Hackathon: 1-Minute Creative Innovation with AI Total Prize: 20,000 USD(USDT) "One-minute creation by AI Coding Autonomous Agent MOUSE-I" Hosted by VIDraft \| Organized by Korea AI Promotion Association (KAIPA) Revolutionary Era of AI Coding "Creating a web app in just one minute" - This is no… ## 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: 2026-11-12 - First published: 2025-06-25 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open VIDraft/AI on Hugging Face](https://huggingface.co/spaces/VIDraft/AI) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b46d1f60-3346-43c9-9491-629c5de74700) - [Its page on Mindket](https://mindket.com/p/b46d1f60-3346-43c9-9491-629c5de74700)