--- title: "Xiaomi-OCR-0-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1921d172-02df-42f8-b8be-4fa52ab31210" updated: "2026-11-13" --- # Xiaomi-OCR-0-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Provides GGUF files of Xiaomi-OCR-0, a compact 0.8B vision-language model for reading documents and text in images, in sizes from about 466 MB to 1.52 GB. For local OCR with llama.cpp. Xiaomi-OCR-0-GGUF is a kit in the Developer 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 prithivMLmods ## About ### What it is This repository holds GGUF files of Xiaomi-OCR-0, a small vision-language model built for OCR and document parsing. It has about 0.8B parameters and is based on Qwen3.5-0.8B-Base. The card says it was trained on a corpus of roughly 170M OCR-focused samples. The files come in several quantization sizes, plus a projection file that lets the model read images. ### Who it suits It suits people who want to turn scans, screenshots and documents into text on their own computer, with a model small enough for modest hardware. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Office work | Administrators | Pull text out of scanned pages | Read a scanned form | | Software | Developers | Add local OCR to an app | Parse a screenshot into text | | Finance | Back-office teams | Read printed receipts and statements | Capture lines from a receipt | | Research | Document AI engineers | Test a compact OCR model | Compare quant sizes | If you need an OCR service with a hosted API or support for many languages, you can skip it, since the card lists English only. ### Where to use it The files run in llama.cpp compatible tools on Windows, macOS or Linux, and the card names llama.cpp as the engine used for the quantization. - llama.cpp and tools built on it - Small laptops, since the files start at about 466 MB - Pictures plus a text prompt as input | Use case | Who | What they get back | | --- | --- | --- | | Document parsing | Developers | Text read from page images | | Screenshot reading | Everyday users | Text taken from a screen capture | | Size testing | Engineers | A comparison across the quant files | ### How to set it up You need Python with pip to fetch the files and a tool that loads GGUF files. Pick one quant file, and add the projection file so the model can read images. - Python with pip - llama.cpp or a similar tool - Less than 2 GB of disk space for any file here 1. Install the Hugging Face hub package. 2. Download the repository, or just the quant and the projection file. 3. Load the quant file in your GGUF tool with the projection file. ```sh pip install -U huggingface_hub hf download prithivMLmods/Xiaomi-OCR-0-GGUF ``` ### How to use it Load the quant file and the projection file in your tool, give it an image of a document and a short instruction, and read the text it returns. The card gives no run command, so follow your tool's instructions. | File | Size | When to pick it | | --- | --- | --- | | BF16 | 1.52 GB | You want full weights and the best quality | | Q6\_K | 630 MB | You want near-full quality | | Q5\_K\_M | 578 MB | You want high quality | | Q4\_K\_M | 529 MB | The balanced default for most uses | | Q4\_K\_S | 505 MB | You want to save a little more space | | Q3\_K\_L | 491 MB | Memory is low and lower quality is fine | | Q3\_K\_M | 466 MB | The smallest file, with lower quality | | mmproj-bf16 | 207 MB | You need the image projection | ### Example You load the Q4\_K\_M file with the projection file and give it a photo of a printed page. You ask it to write out the text, and it answers with the page's words as plain text. ### For newcomers and experts If you are new, start with the Q4\_K\_M file, one clear page photo and a short request to read the text. If you know the field, the original model combines a text anchoring method called Q-Mask with extra pretraining and reinforcement learning across several tasks. Quantization then trades some accuracy for a smaller file. Pick a quant file → Load it with the projection file → Give it a page image → Read the extracted text ### Good to know The smaller quants lose some accuracy, so test a few on your own documents. Check that any private pages stay on your own machine, since the model runs locally. - License: apache-2.0 - Cost to run: none named - Data it reaches: files you download, and the images you give it - Not for: languages other than English, since the card lists English only ## README ````markdown # Xiaomi-OCR-0-GGUF Provides GGUF files of Xiaomi-OCR-0, a compact 0.8B vision-language model for reading documents and text in images, in sizes from about 466 MB to 1.52 GB. For local OCR with llama.cpp. Made by prithivMLmods 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: This repository holds GGUF files of Xiaomi-OCR-0, a small vision-language model built for OCR and document parsing. It has about 0.8B parameters and is based on Qwen3.5-0.8B-Base. The card says it was trained on a corpus of roughly 170M OCR-focused samples. The files come in several quantization sizes, plus a projection file that lets the model read images. Who it suits: It suits people who want to turn scans, screenshots and documents into text on their own computer, with a model small enough for modest hardware. ```table Industry | Who uses it | How they use it | Example Office work | Administrators | Pull text out of scanned pages | Read a scanned form Software | Developers | Add local OCR to an app | Parse a screenshot into text Finance | Back-office teams | Read printed receipts and statements | Capture lines from a receipt Research | Document AI engineers | Test a compact OCR model | Compare quant sizes ``` If you need an OCR service with a hosted API or support for many languages, you can skip it, since the card lists English only. Where to use it: The files run in llama.cpp compatible tools on Windows, macOS or Linux, and the card names llama.cpp as the engine used for the quantization. - llama.cpp and tools built on it - Small laptops, since the files start at about 466 MB - Pictures plus a text prompt as input ```table Use case | Who | What they get back Document parsing | Developers | Text read from page images Screenshot reading | Everyday users | Text taken from a screen capture Size testing | Engineers | A comparison across the quant files ``` How to set it up: You need Python with pip to fetch the files and a tool that loads GGUF files. Pick one quant file, and add the projection file so the model can read images. - Python with pip - llama.cpp or a similar tool - Less than 2 GB of disk space for any file here 1. Install the Hugging Face hub package. 2. Download the repository, or just the quant and the projection file. 3. Load the quant file in your GGUF tool with the projection file. ```sh pip install -U huggingface_hub hf download prithivMLmods/Xiaomi-OCR-0-GGUF ``` How to use it: Load the quant file and the projection file in your tool, give it an image of a document and a short instruction, and read the text it returns. The card gives no run command, so follow your tool's instructions. ```table File | Size | When to pick it BF16 | 1.52 GB | You want full weights and the best quality Q6_K | 630 MB | You want near-full quality Q5_K_M | 578 MB | You want high quality Q4_K_M | 529 MB | The balanced default for most uses Q4_K_S | 505 MB | You want to save a little more space Q3_K_L | 491 MB | Memory is low and lower quality is fine Q3_K_M | 466 MB | The smallest file, with lower quality mmproj-bf16 | 207 MB | You need the image projection ``` Example: You load the Q4_K_M file with the projection file and give it a photo of a printed page. You ask it to write out the text, and it answers with the page's words as plain text. For newcomers and experts: If you are new, start with the Q4_K_M file, one clear page photo and a short request to read the text. If you know the field, the original model combines a text anchoring method called Q-Mask with extra pretraining and reinforcement learning across several tasks. Quantization then trades some accuracy for a smaller file. ```flow Pick a quant file Load it with the projection file Give it a page image Read the extracted text ``` Good to know: The smaller quants lose some accuracy, so test a few on your own documents. Check that any private pages stay on your own machine, since the model runs locally. - License: apache-2.0 - Cost to run: none named - Data it reaches: files you download, and the images you give it - Not for: languages other than English, since the card lists English only ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 752M - Weights: about 376 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 44,340 - Likes: 19 - License: apache-2.0 - Languages: en - Based on: xiaomi-research/Xiaomi-OCR-0 - Tags: transformers, gguf, llama-cpp, document-parsing, document-understanding, visual-question-answering, key-information-extraction, vision-language-model, multimodal, xiaomi, seerray, image-text-to-text ## Links - Model: https://huggingface.co/prithivMLmods/Xiaomi-OCR-0-GGUF - Files: https://huggingface.co/prithivMLmods/Xiaomi-OCR-0-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download prithivMLmods/Xiaomi-OCR-0-GGUF ``` 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 [prithivMLmods/Xiaomi-OCR-0-GGUF on Hugging Face](https://huggingface.co/prithivMLmods/Xiaomi-OCR-0-GGUF) ## 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 | | --- | --- | --- | --- | --- | --- | | Xiaomi-OCR-0-GGUF (this page) | 44,340 | 19 | 752M | apache-2.0 | 2026-10-30 | | [Xiaomi-OCR-0](https://mindket.com/p/85bd3445-61e1-429e-b23c-833b2580414d) | 29,537 | 319 | 873M | apache-2.0 | 2026-10-29 | | [OvisOCR3](https://mindket.com/p/80cf6589-b844-493b-beb2-356ff41f4956) | 203,064 | 121 | 853M | apache-2.0 | 2026-11-10 | | [Mage-VL](https://mindket.com/p/af17eed1-b27e-4e67-92f0-e778e7814e95) | 5,912 | 454 | 4.7B | apache-2.0 | 2026-08-10 | | [Qwen3.8-27B-QSA-GGUF](https://mindket.com/p/84fe4bb3-66d7-4d48-a711-b5d13a08cab6) | 12,136 | 32 | 27.4B | apache-2.0 | 2026-11-05 | | [OvisOCR2](https://mindket.com/p/49bb76fb-b54e-4ee4-b5aa-7971994ca65e) | 28,050 | 504 | 853M | apache-2.0 | 2026-10-22 | 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: 2026-10-30 - First published: 2026-10-07 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open prithivMLmods/Xiaomi-OCR-0-GGUF on Hugging Face](https://huggingface.co/prithivMLmods/Xiaomi-OCR-0-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1921d172-02df-42f8-b8be-4fa52ab31210) - [Its page on Mindket](https://mindket.com/p/1921d172-02df-42f8-b8be-4fa52ab31210)