--- title: "NuExtract3" kind: "Kit" category: "Business" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d90cc768-8f35-406a-81d8-92530ea071d9" updated: "2026-11-13" --- # NuExtract3 > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads documents as text or images and fills a JSON template with the fields you ask for, or converts the pages to Markdown. A 4B model for teams that process scans, forms and invoices. NuExtract3 is a kit in the Business category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by numind ## About ### What it is NuExtract3 is a 4B vision-language model for understanding documents. It does two main jobs. It fills a JSON template with facts pulled from a document, and it converts document images into Markdown. The record lists 4.5B parameters and a base of Qwen3.5-4B. ### Who it suits It suits teams that turn paper-like documents into structured data or searchable text. The card names scans, receipts, forms, invoices, contracts and tables. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Finance and accounting | Back-office teams | Pull fields from invoices and receipts into JSON | Filling a template with dates and totals from a scanned invoice | | Legal | Contract reviewers | Extract named terms from contracts | Listing parties and clauses in a fixed JSON layout | | Software | Builders of search and RAG tools | Convert scanned pages to Markdown before indexing | Turning a form scan into Markdown with tables kept | People who only need plain OCR of simple text can skip it. ### Where to use it - Python with the transformers library on Windows, macOS or Linux - Extraction pipelines that need JSON back - OCR jobs on scans and photos of documents - RAG preprocessing, where pages become Markdown | Use case | Who | What they get back | | --- | --- | --- | | Structured extraction | Data and operations teams | JSON that follows the template you supply | | Image to Markdown | Search and RAG builders | Markdown for text, HTML for tables and LaTeX for math | | Template generation | Analysts | A draft template made from a description or from a document | ### How to set it up - Python on your computer - The transformers library - Disk space for about 9.1 GB of BF16 weights, or about 2.3 GB at 4-bit as an estimate 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load the model with transformers, following the usage notes on the model card. ```sh pip install -U huggingface_hub ``` ```sh hf download numind/NuExtract3 ``` ### How to use it For extraction, you give the model a document as text, images or both, a JSON template and your instructions, and it returns JSON. For conversion, you give it document images and it returns Markdown. The part of the card with run code is not in the text provided here. | Tool | What it does | | --- | --- | | Structured extraction | Fills a JSON template from text or images, guided by your instructions | | Markdown conversion | Turns document images into Markdown | | Reasoning mode | Lets the model think before it answers, or switches that off | | Template generation | Drafts an extraction template from plain language or from a document | ### Example You have a photo of a receipt and a short JSON template with fields for the shop, the date and the total. You pass both with a one-line instruction. The model returns JSON in the shape of your template. ### For newcomers and experts Think of it as a reader that fills in a form for you. A first small step is to download the files and try one clean scan with a simple template. Keep the template short at the start. It is built on Qwen3.5-4B and supports reasoning and non-reasoning inference. On the maker's internal test of about 600 mixed documents, the card reports an average score of 0.651. It also says the reasoning of small models often fell into repetition loops, which hurt their scores. Document as text or images → Add a JSON template and instructions → Model reads the page and reasons → JSON or Markdown comes back ### Good to know - License: Apache 2.0. - Cost to run: none named for the downloaded model. The card also points to a hosted API and platform, with no price given. - Data it reaches: it downloads files from the Hugging Face Hub, then reads whatever documents you give it. - Not for: independent proof. The benchmark is the maker's own, and the card says it plans to publish it later. - Documents in several languages are supported. Average structured extraction score on the maker's benchmark | Label | Value | | --- | --- | | NuExtract3.4 4B RL | 0.651 score | | gemma-4-E4B-it | 0.538 score | | Qwen3.5-9B | 0.479 score | | Qwen3.5-4B | 0.417 score | | Ministral-3-3B | 0.240 score | ## README ````markdown # NuExtract3 Reads documents as text or images and fills a JSON template with the fields you ask for, or converts the pages to Markdown. A 4B model for teams that process scans, forms and invoices. Made by numind 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: NuExtract3 is a 4B vision-language model for understanding documents. It does two main jobs. It fills a JSON template with facts pulled from a document, and it converts document images into Markdown. The record lists 4.5B parameters and a base of Qwen3.5-4B. Who it suits: It suits teams that turn paper-like documents into structured data or searchable text. The card names scans, receipts, forms, invoices, contracts and tables. ```table Industry | Who uses it | How they use it | Example Finance and accounting | Back-office teams | Pull fields from invoices and receipts into JSON | Filling a template with dates and totals from a scanned invoice Legal | Contract reviewers | Extract named terms from contracts | Listing parties and clauses in a fixed JSON layout Software | Builders of search and RAG tools | Convert scanned pages to Markdown before indexing | Turning a form scan into Markdown with tables kept ``` People who only need plain OCR of simple text can skip it. Where to use it: - Python with the transformers library on Windows, macOS or Linux - Extraction pipelines that need JSON back - OCR jobs on scans and photos of documents - RAG preprocessing, where pages become Markdown ```table Use case | Who | What they get back Structured extraction | Data and operations teams | JSON that follows the template you supply Image to Markdown | Search and RAG builders | Markdown for text, HTML for tables and LaTeX for math Template generation | Analysts | A draft template made from a description or from a document ``` How to set it up: - Python on your computer - The transformers library - Disk space for about 9.1 GB of BF16 weights, or about 2.3 GB at 4-bit as an estimate 1. Install the Hugging Face hub package. 2. Download the model files. 3. Load the model with transformers, following the usage notes on the model card. ```sh pip install -U huggingface_hub ``` ```sh hf download numind/NuExtract3 ``` How to use it: For extraction, you give the model a document as text, images or both, a JSON template and your instructions, and it returns JSON. For conversion, you give it document images and it returns Markdown. The part of the card with run code is not in the text provided here. ```table Tool | What it does Structured extraction | Fills a JSON template from text or images, guided by your instructions Markdown conversion | Turns document images into Markdown Reasoning mode | Lets the model think before it answers, or switches that off Template generation | Drafts an extraction template from plain language or from a document ``` Example: You have a photo of a receipt and a short JSON template with fields for the shop, the date and the total. You pass both with a one-line instruction. The model returns JSON in the shape of your template. For newcomers and experts: Think of it as a reader that fills in a form for you. A first small step is to download the files and try one clean scan with a simple template. Keep the template short at the start. It is built on Qwen3.5-4B and supports reasoning and non-reasoning inference. On the maker's internal test of about 600 mixed documents, the card reports an average score of 0.651. It also says the reasoning of small models often fell into repetition loops, which hurt their scores. ```flow Document as text or images Add a JSON template and instructions Model reads the page and reasons JSON or Markdown comes back ``` Good to know: - License: Apache 2.0. - Cost to run: none named for the downloaded model. The card also points to a hosted API and platform, with no price given. - Data it reaches: it downloads files from the Hugging Face Hub, then reads whatever documents you give it. - Not for: independent proof. The benchmark is the maker's own, and the card says it plans to publish it later. - Documents in several languages are supported. ```chart Title: Average structured extraction score on the maker's benchmark NuExtract3.4 4B RL: 0.651 score gemma-4-E4B-it: 0.538 score Qwen3.5-9B: 0.479 score Qwen3.5-4B: 0.417 score Ministral-3-3B: 0.240 score ``` ## On Hugging Face, 13 November 2026 - Task: image to text - Library: transformers - Parameters: 4.5B - Weights: about 9.1 GB in BF16; about 2.3 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 40,915 - Likes: 397 - License: apache-2.0 - Based on: Qwen/Qwen3.5-4B - Quantized versions: 18 - Fine-tunes: 12 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, vision-language, document-understanding, structured-extraction, information-extraction, document-to-markdown, markdown, reasoning, multilingual ## Links - Model: https://huggingface.co/numind/NuExtract3 - Files: https://huggingface.co/numind/NuExtract3/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download numind/NuExtract3 ``` 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 [numind/NuExtract3 on Hugging Face](https://huggingface.co/numind/NuExtract3) ## 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 | | --- | --- | --- | --- | --- | --- | | NuExtract3 (this page) | 40,915 | 397 | 4.5B | apache-2.0 | 2026-08-20 | | [Laplace-1-Atlas](https://mindket.com/p/6546e7f2-1972-480e-9db7-e90bcc3c028a) | 10,692 | 51 | 27.4B | apache-2.0 | 2026-11-04 | | [H2O-Lightning-4B](https://mindket.com/p/f65f5f72-06bb-4b2d-a616-49352aeaa08d) | 10,530 | 107 | 4.7B | apache-2.0 | 2026-11-01 | | [basal-omni-1.0-ultra](https://mindket.com/p/cf9ae713-242d-4145-b344-dc0a8257f403) | 3,831 | 43 | 31.3B | apache-2.0 | 2026-11-07 | | [pplx-decider-v1.1-27b](https://mindket.com/p/f25f6ff8-5eac-4981-ba4e-4bb9366506da) | 4,065 | 182 | 26.1B | apache-2.0 | 2026-10-12 | | [Laplace-1-Vector by JuneoAI](https://mindket.com/p/582c147c-3f50-425a-9034-d702e913d09f) | 10,582 | 44 | 4.1B | apache-2.0 | 2026-11-10 | 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 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · francisrafal · 2026-06-25](https://news.ycombinator.com/item?id=48671675): Show HN: ParseHawk – 100% Local Document AI with API, CLI, and Web UI: I just released ParseHawk v0.1.0: Apache-2.0 licensed 100% local document AI platform that extracts JSON from PDFs, images etc. It builds on top of NuMind's NuExtract3 but additionally enforces a provided JSON schema with constrained decoding. It works on Apple Silicon with pre-bundled vllm-metal… - [Bluesky · probbrain.com · 2026-05-26](https://bsky.app/profile/probbrain.bsky.social/post/3mmquxlp2dx23): numind/NuExtract3 · 148♥ · 20350 dl ## 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: 2026-08-20 - First published: 2026-04-29 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open numind/NuExtract3 on Hugging Face](https://huggingface.co/numind/NuExtract3) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d90cc768-8f35-406a-81d8-92530ea071d9) - [Its page on Mindket](https://mindket.com/p/d90cc768-8f35-406a-81d8-92530ea071d9)