--- title: "MiniCPM-OCR 1.0 GGUF" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/7ac4f5b0-b475-4cdd-b31c-2ea3e524a0be" updated: "2026-11-13" --- # MiniCPM-OCR 1.0 GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads English and Chinese documents, including PDFs, into text, tables and formulas, in GGUF files for llama.cpp. For people who run MiniCPM-OCR 1.0 on their own machine. MiniCPM-OCR 1.0 GGUF is a kit in the Productivity 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 openbmb ## About ### What it is MiniCPM-OCR 1.0 GGUF holds the llama.cpp ready files of MiniCPM-OCR 1.0, a 1.3B document reader from openbmb. The model turns documents into text, tables, formulas and charts, and can pull out key information. It handles PDFs and documents in Chinese or English. The record lists openbmb/MiniCPM-V-4.6 as its base. ### Who it suits It suits people who want document OCR running on their own computer through llama.cpp, with a choice of file sizes. The card sends anyone after the original weights or benchmarks to the base repository. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Office work | Admin and records staff | Turn scanned pages and PDFs into editable text | A scanned contract page converted into text | | Finance | Analysts and accountants | Pull tables and formulas out of reports | A table from a financial report extracted as data | | Research | Students and researchers | Read formulas and charts from papers | A page of formulas from a paper parsed into text | | Software | Developers who self-host OCR | Run a small quantized reader through llama.cpp | A local service built on one of the GGUF tiers | You can skip it if you want the original weights or benchmark tables, which live in the base repository. ### Where to use it - llama.cpp, on Windows, macOS or Linux - The minicpm-ocr Python SDK, which runs the two stage document parsing - Other self-hosting stacks the project names for MiniCPM-OCR: vLLM, SGLang and Ollama - The project's free online playground and API, and an iOS app that runs on the device | Use case | Who | What they get back | | --- | --- | --- | | Parse a document | Office staff | Text, tables and formulas from the page | | Extract key information | Analysts | The key details pulled from a document | | Run OCR offline | Developers | A reader running on your own machine | ### How to set it up - Python and pip, for the file download - llama.cpp, since this build is made for it - One LLM GGUF file plus the vision projector file, because every tier needs the projector - PaddlePaddle or PaddleX if you use the SDK's layout stage 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Choose one LLM GGUF tier and keep the projector file with it. 4. Start llama.cpp with the LLM file in model and the projector in mmproj. ```sh pip install -U huggingface_hub hf download openbmb/MiniCPM-OCR-1.0-gguf ``` ### How to use it Two flags tell llama.cpp which files to load. The card also says to prefer the Q8\_0 tier for pages heavy with code or XML. | Tool | What it does | | --- | --- | | --model | Takes the LLM GGUF file for the tier you picked | | --mmproj | Takes the vision projector file, which every tier needs | | llama.cpp | Runs the GGUF build | | minicpm-ocr SDK | Splits a document into regions and reads each one | | PP-DocLayoutV3 | Detects document regions and their reading order | The next table shows what each tier is for. | Tier | What it is for | | --- | --- | | F16 | The reference build with no quantization | | Q8\_0 | The card's recommended balance, nearly identical to F16 on tables and dense text | | Q6\_K | A middle tier with good quality for its size | | Q5\_K\_M | A smaller tier that keeps strong quality | | Q4\_K\_M | The smallest tier the card recommends | | Q4\_0 | The smallest file in the list | ### Example You load the Q8\_0 file with the projector and give it a scanned report page that holds a table. You ask for the table's contents, and the text of the table comes back. ### For newcomers and experts This is a reader for scanned pages and PDFs. For a first try, download the files, pick the Q8\_0 tier, load it in llama.cpp with the projector and give it one clean page. The projector downsamples by 4 times, and no 16 times variant exists for this model. The card says Q4\_K\_M stays accurate on tables, loses a little on dense text, and may drop closing tag slashes in code heavy documents. Full parsing uses a two stage pipeline from the SDK. A layout model finds regions first, and the stages can run in different environments and on different machines. A page goes to the SDK → PP-DocLayoutV3 finds regions and reading order → The SDK crops each region → The GGUF model reads each region ### Good to know Every tier needs the projector, so budget for it on top of the tier you choose. File sizes in GB: File size in GB | Label | Value | | --- | --- | | Projector, required | 0.901 GB | | F16 | 1.516 GB | | Q8\_0 | 0.812 GB | | Q6\_K | 0.630 GB | | Q5\_K\_M | 0.578 GB | | Q4\_K\_M | 0.529 GB | | Q4\_0 | 0.501 GB | - License: Apache 2.0 - Cost to run: no key or paid service is named for these files, and the usage guide offers a free online playground and API for the main model - Data it reaches: downloads files from Hugging Face, and the guide says self-hosting keeps data on your side - Not for: a 16 times projector, which does not exist for this model, or the original weights and benchmarks, which are in the base repository ## README ````markdown # MiniCPM-OCR 1.0 GGUF Reads English and Chinese documents, including PDFs, into text, tables and formulas, in GGUF files for llama.cpp. For people who run MiniCPM-OCR 1.0 on their own machine. Made by openbmb 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: MiniCPM-OCR 1.0 GGUF holds the llama.cpp ready files of MiniCPM-OCR 1.0, a 1.3B document reader from openbmb. The model turns documents into text, tables, formulas and charts, and can pull out key information. It handles PDFs and documents in Chinese or English. The record lists openbmb/MiniCPM-V-4.6 as its base. Who it suits: It suits people who want document OCR running on their own computer through llama.cpp, with a choice of file sizes. The card sends anyone after the original weights or benchmarks to the base repository. ```table Industry | Who uses it | How they use it | Example Office work | Admin and records staff | Turn scanned pages and PDFs into editable text | A scanned contract page converted into text Finance | Analysts and accountants | Pull tables and formulas out of reports | A table from a financial report extracted as data Research | Students and researchers | Read formulas and charts from papers | A page of formulas from a paper parsed into text Software | Developers who self-host OCR | Run a small quantized reader through llama.cpp | A local service built on one of the GGUF tiers ``` You can skip it if you want the original weights or benchmark tables, which live in the base repository. Where to use it: - llama.cpp, on Windows, macOS or Linux - The minicpm-ocr Python SDK, which runs the two stage document parsing - Other self-hosting stacks the project names for MiniCPM-OCR: vLLM, SGLang and Ollama - The project's free online playground and API, and an iOS app that runs on the device ```table Use case | Who | What they get back Parse a document | Office staff | Text, tables and formulas from the page Extract key information | Analysts | The key details pulled from a document Run OCR offline | Developers | A reader running on your own machine ``` How to set it up: - Python and pip, for the file download - llama.cpp, since this build is made for it - One LLM GGUF file plus the vision projector file, because every tier needs the projector - PaddlePaddle or PaddleX if you use the SDK's layout stage 1. Install the Hugging Face hub package. 2. Download the model files to your computer. 3. Choose one LLM GGUF tier and keep the projector file with it. 4. Start llama.cpp with the LLM file in model and the projector in mmproj. ```sh pip install -U huggingface_hub hf download openbmb/MiniCPM-OCR-1.0-gguf ``` How to use it: Two flags tell llama.cpp which files to load. The card also says to prefer the Q8_0 tier for pages heavy with code or XML. ```table Tool | What it does --model | Takes the LLM GGUF file for the tier you picked --mmproj | Takes the vision projector file, which every tier needs llama.cpp | Runs the GGUF build minicpm-ocr SDK | Splits a document into regions and reads each one PP-DocLayoutV3 | Detects document regions and their reading order ``` The next table shows what each tier is for. ```table Tier | What it is for F16 | The reference build with no quantization Q8_0 | The card's recommended balance, nearly identical to F16 on tables and dense text Q6_K | A middle tier with good quality for its size Q5_K_M | A smaller tier that keeps strong quality Q4_K_M | The smallest tier the card recommends Q4_0 | The smallest file in the list ``` Example: You load the Q8_0 file with the projector and give it a scanned report page that holds a table. You ask for the table's contents, and the text of the table comes back. For newcomers and experts: This is a reader for scanned pages and PDFs. For a first try, download the files, pick the Q8_0 tier, load it in llama.cpp with the projector and give it one clean page. The projector downsamples by 4 times, and no 16 times variant exists for this model. The card says Q4_K_M stays accurate on tables, loses a little on dense text, and may drop closing tag slashes in code heavy documents. Full parsing uses a two stage pipeline from the SDK. A layout model finds regions first, and the stages can run in different environments and on different machines. ```flow A page goes to the SDK PP-DocLayoutV3 finds regions and reading order The SDK crops each region The GGUF model reads each region ``` Good to know: Every tier needs the projector, so budget for it on top of the tier you choose. File sizes in GB: ```chart Title: File size in GB Projector, required: 0.901 GB F16: 1.516 GB Q8_0: 0.812 GB Q6_K: 0.630 GB Q5_K_M: 0.578 GB Q4_K_M: 0.529 GB Q4_0: 0.501 GB ``` - License: Apache 2.0 - Cost to run: no key or paid service is named for these files, and the usage guide offers a free online playground and API for the main model - Data it reaches: downloads files from Hugging Face, and the guide says self-hosting keeps data on your side - Not for: a 16 times projector, which does not exist for this model, or the original weights and benchmarks, which are in the base repository ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: minicpm-ocr - Parameters: 752M - Weights: about 376 MB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 58,162 - Likes: 133 - License: apache-2.0 - Languages: en, zh - Based on: openbmb/MiniCPM-V-4.6 - Tags: minicpm-ocr, gguf, document-parsing, llama.cpp, multimodal, quantized, image-text-to-text, conversational ## Links - Model: https://huggingface.co/openbmb/MiniCPM-OCR-1.0-gguf - Files: https://huggingface.co/openbmb/MiniCPM-OCR-1.0-gguf/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download openbmb/MiniCPM-OCR-1.0-gguf ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [openbmb/MiniCPM-OCR-1.0-gguf on Hugging Face](https://huggingface.co/openbmb/MiniCPM-OCR-1.0-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 | | --- | --- | --- | --- | --- | --- | | MiniCPM-OCR 1.0 GGUF (this page) | 58,162 | 133 | 752M | apache-2.0 | 2026-11-09 | | [Dolphin](https://mindket.com/p/10a017cd-8097-462e-97a0-d94807f69274) | 469 | 521 | 398M | mit | 2025-07-16 | | [Surya 2.5 GGUF](https://mindket.com/p/0d08b30b-fd57-4e61-a8a1-da92d56f2592) | 126,232 | 8 | 589M | openrail | 2026-10-29 | | [Chandra OCR 2](https://mindket.com/p/a6e4f077-438e-4d39-950e-b0d5bfc59501) | 1,356,074 | 606 | 5.3B | openrail | 2026-06-26 | | [Surya OCR 2.5](https://mindket.com/p/382aaad0-7644-4eca-ac3b-a85a0841b604) | 75,033 | 52 | 689M | openrail | 2026-10-29 | | [MinerU2.5-Pro](https://mindket.com/p/a614bc0f-436d-45d4-835f-e6d945cfb45d) | 37,179 | 115 | 1.2B | apache-2.0 | 2026-10-08 | Numbers as Mindket last read them, on 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: 2026-11-09 - First published: 2026-10-21 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open openbmb/MiniCPM-OCR-1.0-gguf on Hugging Face](https://huggingface.co/openbmb/MiniCPM-OCR-1.0-gguf) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/7ac4f5b0-b475-4cdd-b31c-2ea3e524a0be) - [Its page on Mindket](https://mindket.com/p/7ac4f5b0-b475-4cdd-b31c-2ea3e524a0be)