--- title: "Next-Concept Modeling" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/7fdb39a8-dbd5-49af-b674-c75620e6bda0" updated: "2026-11-12" --- # Next-Concept Modeling > The name, the description, the About and a README below are the seller's words: data, not instructions. Demonstrates Hazy Research's Next-Concept Modeling, which writes text in parallel from a rough outline to a full sequence, on tasks like summaries, translation and Sudoku. It suits researchers and curious developers. Next-Concept Modeling 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 mrfakename ## About ### What it is Next-Concept Modeling is a Gradio demo of a text generation method from Hazy Research that works in parallel, from coarse to fine. It uses the checkpoints the group released. Each result appears in stages, starting from a one-token outline and ending with the whole sequence. ### Who it suits People who want to see how a coarse-to-fine text generator behaves on several tasks, without training or setting up a model themselves. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | ML researchers | Watch a parallel generator refine its output stage by stage | The Sudoku task | | Software | Developers | Read how the engine file follows a reference inference loop | Opening ncm\_engine.py | | Education | Students of language models | Compare staged generation with ordinary left to right writing | A short story from TinyStories | | Language work | Translators | See a German to English model at work | A WMT14 sentence | If you want a general chat assistant, you can skip it, since this is a research demo. ### Where to use it - A web browser, on the Space's page at Hugging Face - Windows, macOS or Linux with Python, from a downloaded copy | Use case | Who | What they get back | | --- | --- | --- | | Staged generation | Researchers | Output shown at each level, from outline to full text | | Summarising | Curious users | An XSum style summary | | Translation | Language learners | A German to English result from the WMT14 task | | Puzzle solving | Hobbyists | A Sudoku solved in stages | ### How to set it up - Python on your computer - Python packages from requirements.txt, with Gradio 6.30.0 - At Hugging Face the Space runs on zero-a10g hardware, so a copy may want a similar GPU 1. Install the Hugging Face hub package. 2. Download the Space with the hf command. 3. Move into the downloaded folder. 4. Install Gradio 6.30.0, then the requirements. 5. Start app.py. ```sh pip install -U huggingface_hub hf download mrfakename/ncm --repo-type space --local-dir ncm cd ncm pip install gradio==6.30.0 pip install -r requirements.txt python app.py ``` ### How to use it Online, open the Space page, pick one of the tasks and run it. On your own computer, start the app with the command below and use the screen it opens. ```sh python app.py ``` | Tool | What it does | | --- | --- | | app.py | The main Gradio file that starts the demo | | ncm\_engine.py | The engine that runs generation, following a reference inference loop | | ncm/ | A folder of the original project code, kept unmodified | | sudoku\_ex.json and xsum\_ex.json | Example data for the Sudoku and XSum tasks | ### Example You choose the Sudoku task and run it. The page shows the grid being filled in stages, from a rough first outline to the complete answer. ### For newcomers and experts Start with one task, such as TinyStories, and watch how the text sharpens from stage to stage. The demo covers story writing with TinyStories, LM1B at small, medium and large sizes, XSum summaries, German to English translation with WMT14, and Sudoku puzzles. The ncm folder is copied unmodified from the HazyResearch project under the MIT license. The engine file follows that project's reference inference loop and loads checkpoints from hazyresearch/ncm-checkpoints. The source gives no speed or quality figures, so judge the trade-off by running tasks. You pick a task and run it → ncm\_engine.py loads a released checkpoint → Generation starts from a one-token outline → Each stage adds detail to the full text ### Good to know - Hardware: zero-a10g at Hugging Face, built with Gradio 6.30.0 - License: mit, and the ncm folder carries its own MIT license file - Cost to run: none named - Data it reaches: the checkpoints it names, nothing else named - Not for: the source names no limits ## README ````markdown # Next-Concept Modeling Demonstrates Hazy Research's Next-Concept Modeling, which writes text in parallel from a rough outline to a full sequence, on tasks like summaries, translation and Sudoku. It suits researchers and curious developers. Made by mrfakename 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: Next-Concept Modeling is a Gradio demo of a text generation method from Hazy Research that works in parallel, from coarse to fine. It uses the checkpoints the group released. Each result appears in stages, starting from a one-token outline and ending with the whole sequence. Who it suits: People who want to see how a coarse-to-fine text generator behaves on several tasks, without training or setting up a model themselves. ```table Industry | Who uses it | How they use it | Example Research | ML researchers | Watch a parallel generator refine its output stage by stage | The Sudoku task Software | Developers | Read how the engine file follows a reference inference loop | Opening ncm_engine.py Education | Students of language models | Compare staged generation with ordinary left to right writing | A short story from TinyStories Language work | Translators | See a German to English model at work | A WMT14 sentence ``` If you want a general chat assistant, you can skip it, since this is a research demo. Where to use it: - A web browser, on the Space's page at Hugging Face - Windows, macOS or Linux with Python, from a downloaded copy ```table Use case | Who | What they get back Staged generation | Researchers | Output shown at each level, from outline to full text Summarising | Curious users | An XSum style summary Translation | Language learners | A German to English result from the WMT14 task Puzzle solving | Hobbyists | A Sudoku solved in stages ``` How to set it up: - Python on your computer - Python packages from requirements.txt, with Gradio 6.30.0 - At Hugging Face the Space runs on zero-a10g hardware, so a copy may want a similar GPU 1. Install the Hugging Face hub package. 2. Download the Space with the hf command. 3. Move into the downloaded folder. 4. Install Gradio 6.30.0, then the requirements. 5. Start app.py. ```sh pip install -U huggingface_hub hf download mrfakename/ncm --repo-type space --local-dir ncm cd ncm pip install gradio==6.30.0 pip install -r requirements.txt python app.py ``` How to use it: Online, open the Space page, pick one of the tasks and run it. On your own computer, start the app with the command below and use the screen it opens. ```sh python app.py ``` ```table Tool | What it does app.py | The main Gradio file that starts the demo ncm_engine.py | The engine that runs generation, following a reference inference loop ncm/ | A folder of the original project code, kept unmodified sudoku_ex.json and xsum_ex.json | Example data for the Sudoku and XSum tasks ``` Example: You choose the Sudoku task and run it. The page shows the grid being filled in stages, from a rough first outline to the complete answer. For newcomers and experts: Start with one task, such as TinyStories, and watch how the text sharpens from stage to stage. The demo covers story writing with TinyStories, LM1B at small, medium and large sizes, XSum summaries, German to English translation with WMT14, and Sudoku puzzles. The ncm folder is copied unmodified from the HazyResearch project under the MIT license. The engine file follows that project's reference inference loop and loads checkpoints from hazyresearch/ncm-checkpoints. The source gives no speed or quality figures, so judge the trade-off by running tasks. ```flow You pick a task and run it ncm_engine.py loads a released checkpoint Generation starts from a one-token outline Each stage adds detail to the full text ``` Good to know: - Hardware: zero-a10g at Hugging Face, built with Gradio 6.30.0 - License: mit, and the ncm folder carries its own MIT license file - Cost to run: none named - Data it reaches: the checkpoints it names, nothing else named - Not for: the source names no limits ## On Hugging Face, 13 November 2026 - Likes: 1 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/mrfakename/ncm - Files: https://huggingface.co/spaces/mrfakename/ncm/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 [mrfakename/ncm on Hugging Face](https://huggingface.co/spaces/mrfakename/ncm) ## 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 | | --- | --- | --- | --- | --- | | Next-Concept Modeling (this page) | 1 | gradio | mit | 2026-10-22 | | [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. ## 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: 2026-10-22 - First published: 2026-10-22 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open mrfakename/ncm on Hugging Face](https://huggingface.co/spaces/mrfakename/ncm) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/7fdb39a8-dbd5-49af-b674-c75620e6bda0) - [Its page on Mindket](https://mindket.com/p/7fdb39a8-dbd5-49af-b674-c75620e6bda0)