--- title: "Xiaomi OCR 0 Demo" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5e258cbb-f4f5-4904-aad6-78f5e9a566f4" updated: "2026-11-12" --- # Xiaomi OCR 0 Demo > The name, the description, the About and a README below are the seller's words: data, not instructions. Parses images and PDFs, extracts fields and answers questions about documents with the Xiaomi-OCR-0 model, in a Docker-based document workbench that needs a GPU. Xiaomi OCR 0 Demo is a kit in the Productivity 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 loongwayX ## About ### What it is Xiaomi OCR 0 Demo is a document workbench that parses images and PDFs, extracts key information and answers questions about a document. It serves the Xiaomi-OCR-0 model with vLLM on the Space's GPU, next to two separate PP-DocLayoutV3 layout workers. It runs from a Docker image on port 7860. ### Who it suits People who handle scanned or photographed documents and want text, fields and answers out of them, and engineers studying a document AI stack. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Finance | Back-office teams | Pull fields from invoices and forms | Upload a PDF and read the extracted fields | | Legal | Paralegals | Read the text of scanned pages | Parse a scanned contract page | | Operations | Records teams | Turn paper into searchable text | Process a photo of a page | | Software | Engineers | Study a vLLM and layout worker setup | Read layout\_server.py and start.py | People whose documents are confidential should skip the hosted Space, because uploads are archived and relayed as described below. ### Where to use it - A browser, by visiting the Space on Hugging Face - Windows, macOS or Linux with Docker, for a copy on your own computer - Port 7860 on your machine, once the container runs | Use case | Who | What they get back | | --- | --- | --- | | Parse an image or PDF | Document teams | The parsed content of the page | | Extract key information | Finance and operations | Fields pulled from the document | | Ask about a document | Anyone | An answer from visual question answering | ### How to set it up - Docker on Windows, macOS or Linux - Python and pip, only to fetch the files - A GPU, since the README recommends an A10G large with 24 GB of GPU memory, 12 vCPU and 46 GB of RAM - A first startup that downloads the model weights 1. Install the Hugging Face hub package. 2. Download the Space with the hf command. 3. Move into the new folder. 4. Build the Docker image. 5. Run the container and publish port 7860. ```sh pip install -U huggingface_hub hf download loongwayX/Xiaomi-OCR-0-demo --repo-type space --local-dir Xiaomi-OCR-0-demo cd Xiaomi-OCR-0-demo docker build -t xiaomi-ocr-0-demo . docker run -it -p 7860:7860 xiaomi-ocr-0-demo ``` ### How to use it Online, visit the Space on Hugging Face and add an image or PDF to the workbench. With a local container, open port 7860 in your browser once the model and both layout workers report ready. | Variable | Default | | --- | --- | | XIAOMI\_OCR\_MODEL | SeerRay-Lab/Xiaomi-OCR-0 | | MAX\_MODEL\_LEN | 16384 | | MAX\_NUM\_SEQS | 4 | | GPU\_MEMORY\_UTILIZATION | 0.65 | | REGION\_CONCURRENCY | 4 | | LAYOUT\_CPU\_THREADS | 2 per layout worker | ### Example You add a PDF to the workbench and ask for its key fields. The app parses the pages with the layout workers, reads them with the model, and returns the extracted information. ### For newcomers and experts If you are new, start with one image of a printed page and ask for a plain parse. A page that is clear and upright gives the easiest first result. If you know the field, the stack runs vLLM 0.19.1 with CUDA 12 PyTorch 2.10 and Transformers 5.8.1, while PaddlePaddle GPU 3.3.1 and PaddleX 3.7.2 live in a separate environment. Both layout workers share GPU 0 with vLLM and take requests when idle. It starts with BF16 precision, a context of about 16 thousand tokens, four sequences at a time and four region workers. You add an image or PDF → Layout workers find the regions → Xiaomi-OCR-0 reads them through vLLM → You get text, fields or an answer ### Good to know - Startup: the public port opens only after the model and both layout workers are ready - Weights: they download on first startup, and caches may need to be fetched again after a rebuild - License: apache-2 - Cost to run: a GPU such as an A10G large, no API key or paid service is named - Data it reaches: on the hosted Space, uploads are archived locally, and a relay copies inputs and OCR results to Xiaomi FDS, with no document data sent to a Hugging Face dataset - Not for: confidential documents on the hosted Space, because of the upload archive ## README ````markdown # Xiaomi OCR 0 Demo Parses images and PDFs, extracts fields and answers questions about documents with the Xiaomi-OCR-0 model, in a Docker-based document workbench that needs a GPU. Made by loongwayX 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: Xiaomi OCR 0 Demo is a document workbench that parses images and PDFs, extracts key information and answers questions about a document. It serves the Xiaomi-OCR-0 model with vLLM on the Space's GPU, next to two separate PP-DocLayoutV3 layout workers. It runs from a Docker image on port 7860. Who it suits: People who handle scanned or photographed documents and want text, fields and answers out of them, and engineers studying a document AI stack. ```table Industry | Who uses it | How they use it | Example Finance | Back-office teams | Pull fields from invoices and forms | Upload a PDF and read the extracted fields Legal | Paralegals | Read the text of scanned pages | Parse a scanned contract page Operations | Records teams | Turn paper into searchable text | Process a photo of a page Software | Engineers | Study a vLLM and layout worker setup | Read layout_server.py and start.py ``` People whose documents are confidential should skip the hosted Space, because uploads are archived and relayed as described below. Where to use it: - A browser, by visiting the Space on Hugging Face - Windows, macOS or Linux with Docker, for a copy on your own computer - Port 7860 on your machine, once the container runs ```table Use case | Who | What they get back Parse an image or PDF | Document teams | The parsed content of the page Extract key information | Finance and operations | Fields pulled from the document Ask about a document | Anyone | An answer from visual question answering ``` How to set it up: - Docker on Windows, macOS or Linux - Python and pip, only to fetch the files - A GPU, since the README recommends an A10G large with 24 GB of GPU memory, 12 vCPU and 46 GB of RAM - A first startup that downloads the model weights 1. Install the Hugging Face hub package. 2. Download the Space with the hf command. 3. Move into the new folder. 4. Build the Docker image. 5. Run the container and publish port 7860. ```sh pip install -U huggingface_hub hf download loongwayX/Xiaomi-OCR-0-demo --repo-type space --local-dir Xiaomi-OCR-0-demo cd Xiaomi-OCR-0-demo docker build -t xiaomi-ocr-0-demo . docker run -it -p 7860:7860 xiaomi-ocr-0-demo ``` How to use it: Online, visit the Space on Hugging Face and add an image or PDF to the workbench. With a local container, open port 7860 in your browser once the model and both layout workers report ready. ```table Variable | Default XIAOMI_OCR_MODEL | SeerRay-Lab/Xiaomi-OCR-0 MAX_MODEL_LEN | 16384 MAX_NUM_SEQS | 4 GPU_MEMORY_UTILIZATION | 0.65 REGION_CONCURRENCY | 4 LAYOUT_CPU_THREADS | 2 per layout worker ``` Example: You add a PDF to the workbench and ask for its key fields. The app parses the pages with the layout workers, reads them with the model, and returns the extracted information. For newcomers and experts: If you are new, start with one image of a printed page and ask for a plain parse. A page that is clear and upright gives the easiest first result. If you know the field, the stack runs vLLM 0.19.1 with CUDA 12 PyTorch 2.10 and Transformers 5.8.1, while PaddlePaddle GPU 3.3.1 and PaddleX 3.7.2 live in a separate environment. Both layout workers share GPU 0 with vLLM and take requests when idle. It starts with BF16 precision, a context of about 16 thousand tokens, four sequences at a time and four region workers. ```flow You add an image or PDF Layout workers find the regions Xiaomi-OCR-0 reads them through vLLM You get text, fields or an answer ``` Good to know: - Startup: the public port opens only after the model and both layout workers are ready - Weights: they download on first startup, and caches may need to be fetched again after a rebuild - License: apache-2 - Cost to run: a GPU such as an A10G large, no API key or paid service is named - Data it reaches: on the hosted Space, uploads are archived locally, and a relay copies inputs and OCR results to Xiaomi FDS, with no document data sent to a Hugging Face dataset - Not for: confidential documents on the hosted Space, because of the upload archive ## On Hugging Face, 13 November 2026 - Likes: 18 - Built with: docker - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/loongwayX/Xiaomi-OCR-0-demo - Files: https://huggingface.co/spaces/loongwayX/Xiaomi-OCR-0-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, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [loongwayX/Xiaomi-OCR-0-demo on Hugging Face](https://huggingface.co/spaces/loongwayX/Xiaomi-OCR-0-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 | | --- | --- | --- | --- | --- | | Xiaomi OCR 0 Demo (this page) | 18 | docker | apache-2.0 | 2026-10-15 | | [AI Jukebox](https://mindket.com/p/9dba893e-7854-4e10-8317-8a1ddf622a13) | 426 | docker | mit | 2024-09-20 | | [AUDIO](https://mindket.com/p/99258ed2-9cc9-4151-bd2a-93ec1f744c3c) | 25 | docker | apache-2.0 | 2026-06-18 | | [Swap Face Model](https://mindket.com/p/c5fb31d3-6921-4fd4-9257-74b965885c66) | 21 | docker | mit | 2026-04-04 | | [Image To Relief](https://mindket.com/p/3097f3d6-73a2-4ae1-9104-d20688e451e4) | 3 | docker | mit | 2026-08-05 | | [Hermes Agent by Mega-Evolutions](https://mindket.com/p/17fe54b7-9195-4f76-9f4c-ab7299075987) | 3 | docker | mit | 2026-11-03 | 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. - [Bluesky · AI без границ · 2026-11-02](https://bsky.app/profile/aibiznes.bsky.social/post/3mzcngyojo62t): Xiaomi выпустила компактную модель для работы с документами Xiaomi открыла Xiaomi-OCR-0 — модель на 0,8 млрд параметров, которая... https://github.com/xiaomi-research/Xiaomi-OCR-0 https://huggingface.co/xiaomi-research/Xiaomi-OCR-0 https://huggingface.co/spaces/loongwayX/Xiaomi-OCR-0-demo ## 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: 2026-10-15 - First published: 2026-10-15 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open loongwayX/Xiaomi-OCR-0-demo on Hugging Face](https://huggingface.co/spaces/loongwayX/Xiaomi-OCR-0-demo) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5e258cbb-f4f5-4904-aad6-78f5e9a566f4) - [Its page on Mindket](https://mindket.com/p/5e258cbb-f4f5-4904-aad6-78f5e9a566f4)