--- title: "NCM checkpoints" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/09fee197-d074-47e8-93fb-a98911c715af" updated: "2026-11-12" --- # NCM checkpoints > The name, the description, the About and a README below are the seller's words: data, not instructions. Research checkpoints from Hazy Research for Next-Concept Modeling, a coarse-to-fine text generation method, with tokenizer and generator pairs for summarization, translation, Sudoku and language modeling. NCM checkpoints 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 hazyresearch ## About ### What it is Hazy Research published trained model pairs for Next-Concept Modeling, each pairing a tokenizer with a generator, and this repository holds them. Its paper describes the method as fast, parallel text generation that goes from coarse to fine. Each folder holds a config.yaml file and the .pt checkpoint files it names. The config records the architecture and the sampling settings. ### Who it suits It suits researchers who want to reproduce or study Next-Concept Modeling, and engineers who follow new text generation methods. It is a research release, not a chat assistant. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | NLP researchers | Reproduce results from the paper | Loading the summarization pair | | Machine learning | Generation method developers | Compare coarse-to-fine decoding with standard decoding | Testing the language modeling pairs | | Education | Students of generative models | Study a published method with its checkpoints | Reading a config and its checkpoint | | Puzzles | Hobbyist experimenters | Try the Sudoku checkpoints | Running the easy puzzle set | Skip it if you want a ready chat model, since these checkpoints serve specific research tasks. ### Where to use it - The project's own code repository, which the model card links - Python on Windows, macOS or Linux, for the download step - Research setups that follow the Next-Concept Modeling paper | Use case | Who | What they get back | | --- | --- | --- | | Summarization | Researchers | Checkpoints trained on the XSum task | | Translation | Researchers | Checkpoints for German to English on WMT14 | | Language modeling | Researchers | Small, medium and large models on One Billion Word, plus TinyStories and OpenWebText | ### How to set it up You need a few things in place first. - Python and pip, for the download command - The project's code, which reads the config and checkpoint files - Disk space for the folders you choose 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Open the folder for your task and point the project's code at its config file. ```sh pip install -U huggingface_hub hf download hazyresearch/ncm-checkpoints ``` ### How to use it You pick the folder that matches your task and use its config.yaml with the project's code. The model card shows no run commands, so the code repository holds the details. The folders and their tasks are listed below. | Folder | Task | | --- | --- | | xsum | XSum summarization | | wmt14 | WMT14 German to English translation | | sudoku | Sudoku, in easy, medium and hard sets | | lm1b | One Billion Word, in small, medium and large sizes with a shared tokenizer | | tinystories | TinyStories | | owt | OpenWebText | ### Example You want to try summarization. You download the repository and open the xsum folder. There you find a config.yaml and the checkpoint files it names, which the project's code loads to generate a summary. ### For newcomers and experts Start by reading the paper summary on the project page and the config.yaml in one folder. Pick a single task, such as the TinyStories folder, and get that one running before you try the others. The pairs combine a tokenizer and a generator. The title of the paper describes the aim as fast, parallel, coarse-to-fine generation, so a concept level comes first and finer detail follows. The card gives no benchmark numbers or sizes, so read the paper for results. Pick the folder for your task → Read its config.yaml → Load the named checkpoints → Generate text with the project's code ### Good to know - License: MIT - Cost to run: none named - Data it reaches: it downloads files from Hugging Face onto your computer - Not for: chat or instruction following, since these are research checkpoints for specific tasks ## README ````markdown # NCM checkpoints Research checkpoints from Hazy Research for Next-Concept Modeling, a coarse-to-fine text generation method, with tokenizer and generator pairs for summarization, translation, Sudoku and language modeling. Made by hazyresearch 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: Hazy Research published trained model pairs for Next-Concept Modeling, each pairing a tokenizer with a generator, and this repository holds them. Its paper describes the method as fast, parallel text generation that goes from coarse to fine. Each folder holds a config.yaml file and the .pt checkpoint files it names. The config records the architecture and the sampling settings. Who it suits: It suits researchers who want to reproduce or study Next-Concept Modeling, and engineers who follow new text generation methods. It is a research release, not a chat assistant. ```table Industry | Who uses it | How they use it | Example Research | NLP researchers | Reproduce results from the paper | Loading the summarization pair Machine learning | Generation method developers | Compare coarse-to-fine decoding with standard decoding | Testing the language modeling pairs Education | Students of generative models | Study a published method with its checkpoints | Reading a config and its checkpoint Puzzles | Hobbyist experimenters | Try the Sudoku checkpoints | Running the easy puzzle set ``` Skip it if you want a ready chat model, since these checkpoints serve specific research tasks. Where to use it: - The project's own code repository, which the model card links - Python on Windows, macOS or Linux, for the download step - Research setups that follow the Next-Concept Modeling paper ```table Use case | Who | What they get back Summarization | Researchers | Checkpoints trained on the XSum task Translation | Researchers | Checkpoints for German to English on WMT14 Language modeling | Researchers | Small, medium and large models on One Billion Word, plus TinyStories and OpenWebText ``` How to set it up: You need a few things in place first. - Python and pip, for the download command - The project's code, which reads the config and checkpoint files - Disk space for the folders you choose 1. Install the Hugging Face hub package with pip. 2. Download the repository with the hf command. 3. Open the folder for your task and point the project's code at its config file. ```sh pip install -U huggingface_hub hf download hazyresearch/ncm-checkpoints ``` How to use it: You pick the folder that matches your task and use its config.yaml with the project's code. The model card shows no run commands, so the code repository holds the details. The folders and their tasks are listed below. ```table Folder | Task xsum | XSum summarization wmt14 | WMT14 German to English translation sudoku | Sudoku, in easy, medium and hard sets lm1b | One Billion Word, in small, medium and large sizes with a shared tokenizer tinystories | TinyStories owt | OpenWebText ``` Example: You want to try summarization. You download the repository and open the xsum folder. There you find a config.yaml and the checkpoint files it names, which the project's code loads to generate a summary. For newcomers and experts: Start by reading the paper summary on the project page and the config.yaml in one folder. Pick a single task, such as the TinyStories folder, and get that one running before you try the others. The pairs combine a tokenizer and a generator. The title of the paper describes the aim as fast, parallel, coarse-to-fine generation, so a concept level comes first and finer detail follows. The card gives no benchmark numbers or sizes, so read the paper for results. ```flow Pick the folder for your task Read its config.yaml Load the named checkpoints Generate text with the project's code ``` Good to know: - License: MIT - Cost to run: none named - Data it reaches: it downloads files from Hugging Face onto your computer - Not for: chat or instruction following, since these are research checkpoints for specific tasks ## On Hugging Face, 13 November 2026 - Task: text generation - Downloads in the last 30 days: 0 - Likes: 74 - License: mit - Quantized versions: 1 - Fine-tunes: 3 - Tags: text-generation ## Links - Model: https://huggingface.co/hazyresearch/ncm-checkpoints - Files: https://huggingface.co/hazyresearch/ncm-checkpoints/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download hazyresearch/ncm-checkpoints ``` Then follow the usage in its model card. 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 [hazyresearch/ncm-checkpoints on Hugging Face](https://huggingface.co/hazyresearch/ncm-checkpoints) ## 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 | | --- | --- | --- | --- | --- | --- | | NCM checkpoints (this page) | 0 | 74 | — | mit | 2026-10-16 | | [GPT-2](https://mindket.com/p/c513092f-bbe7-4baa-989f-dfdc12a2b725) | 32,892,223 | 3,932 | 137M | mit | 2024-02-19 | | [Qwen3-0.6B](https://mindket.com/p/0598bfe0-640a-4bf0-b9f4-ca4cf70a04ae) | 19,718,599 | 1,894 | 752M | apache-2.0 | 2025-07-26 | | [Qwen3-14B](https://mindket.com/p/56074bfc-f2ce-4109-8314-1af039530db4) | 12,965,376 | 540 | 14.8B | apache-2.0 | 2025-07-26 | | [Qwen3-Embedding-0.6B](https://mindket.com/p/13d9351b-4e94-46a7-b262-1d8b53353c54) | 8,761,091 | 1,346 | 596M | apache-2.0 | 2026-04-20 | | [DeepSeek-V3.2](https://mindket.com/p/dd611876-a291-4d03-855d-a058431c4303) | 8,260,197 | 1,597 | 685B | mit | 2025-12-01 | 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: 2026-10-16 - First published: 2026-10-13 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open hazyresearch/ncm-checkpoints on Hugging Face](https://huggingface.co/hazyresearch/ncm-checkpoints) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/09fee197-d074-47e8-93fb-a98911c715af) - [Its page on Mindket](https://mindket.com/p/09fee197-d074-47e8-93fb-a98911c715af)