--- title: "Unlimited-OCR" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/7d4ef21c-2833-4f46-80ea-14b788324a91" updated: "2026-11-12" --- # Unlimited-OCR > The name, the description, the About and a README below are the seller's words: data, not instructions. Parses document images and multi-page PDFs into text with a 3.3B vision-language model from Baidu. For developers who add OCR and document parsing to their own tools. Unlimited-OCR 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 baidu ## About ### What it is Unlimited-OCR is a 3.3B-parameter image-to-text model from Baidu for reading documents. You give it a page image, several page images or a PDF turned into images, and it parses them into text. The authors present it as a step beyond Deepseek-OCR, and it runs through the transformers library. ### Who it suits It suits developers and teams who turn scanned pages and multi-page documents into text inside their own code. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Finance and legal | Operations teams | Parse scanned multi-page documents into text | A stack of scanned contract pages read in one call | | Publishing and archives | Archivists | Digitize page images | Old scans turned into searchable text | | Software | Developers | Add document parsing to an app | A service that calls the model on each uploaded page | | AI research | Researchers | Study long-document parsing | Compare its output with Deepseek-OCR on the same pages | If you have no NVIDIA GPU, the transformers example here will not run as written, so you may want to skip it. ### Where to use it - Python with the transformers library on NVIDIA GPUs - vLLM, which the project lists as supported for inference - ms-swift, which the project lists as supported for training - Baidu Cloud and ModelScope, where the project says the model is available - A Hugging Face Spaces demo, if you want to try it without installing anything | Use case | Who | What they get back | | --- | --- | --- | | Single page parsing | Developers | Text parsed from one image, saved to the output folder you name | | Multi-page parsing | Document teams | Parsed text for several page images in one call | | PDF parsing | Data teams | Parsed text for a PDF after its pages become images | ### How to set it up You need these first: - Python and an NVIDIA GPU, since the example moves the model to CUDA - The package versions below, which the project tested with Python 3.12.3 and CUDA 12.9 - About 6.7 GB of disk for the BF16 weights 1. Install the pinned package versions with pip. 2. Download the model files with the hub tool. 3. Load the tokenizer and model with the Python code below. ```text torch==2.10.0 torchvision==0.25.0 transformers==4.57.1 Pillow==12.1.1 matplotlib==3.10.8 einops==0.8.2 addict==2.4.0 easydict==1.13 pymupdf==1.27.2.2 psutil==7.2.2 ``` ```sh pip install -U huggingface_hub hf download baidu/Unlimited-OCR ``` ```python import os import torch from transformers import AutoModel, AutoTokenizer model_name = 'baidu/Unlimited-OCR' tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModel.from_pretrained( model_name, trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16, ) model = model.eval().cuda() ``` ### How to use it After loading, call infer on one image, or infer\_multi on several page images. A single image can use the gundam setting or the base setting. Several pages and PDFs use the base size only. ```python model.infer( tokenizer, prompt='document parsing.', image_file='your_image.jpg', output_path='your/output/dir', base_size=1024, image_size=640, crop_mode=True, max_length=32768, no_repeat_ngram_size=35, ngram_window=128, save_results=True, ) ``` | Tool | What it does | | --- | --- | | model.infer | Parses one image with the gundam or the base setting | | model.infer\_multi | Parses several page images in one call at image size 1024 | | PDF route | Turns each PDF page into an image with PyMuPDF at 300 dpi, then calls infer\_multi | ### Example You pass three page images, page1.png to page3.png, to infer\_multi with the multi-page prompt. The model parses them together and saves the results to the output folder you set. ### For newcomers and experts This is an OCR model: it reads text out of pictures of pages. A first small thing to try is the Hugging Face Spaces demo, then the single-image example once the packages are installed. Under the hood, gundam turns crop mode on at image size 640, while base turns it off at 1024. The examples set a maximum length of 32768 and a no-repeat n-gram size of 35, with a wider window for multi-page runs. The weights are BF16, and the 4-bit size is an estimate. Page images or PDF pages go in → A prompt tells the model what to parse → The model reads the pages on the GPU → Parsed results save to your output folder ### Good to know Weight size depends on precision, and the 4-bit figure is an estimate. Weight size by precision | Label | Value | | --- | --- | | BF16 | 6.7 GB | | 4-bit estimate | 1.7 GB | - Remote code: the example sets trust\_remote\_code, so it runs code that ships with the model - Hardware: the transformers example targets NVIDIA GPUs - Other versions: 40 quantized and 16 fine-tuned versions made by other people are listed - License: MIT - Cost to run: none named, you supply the GPU - Data it reaches: the images and PDFs you point it at, and the output folder it writes to ## README ````markdown # Unlimited-OCR Parses document images and multi-page PDFs into text with a 3.3B vision-language model from Baidu. For developers who add OCR and document parsing to their own tools. Made by baidu 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: Unlimited-OCR is a 3.3B-parameter image-to-text model from Baidu for reading documents. You give it a page image, several page images or a PDF turned into images, and it parses them into text. The authors present it as a step beyond Deepseek-OCR, and it runs through the transformers library. Who it suits: It suits developers and teams who turn scanned pages and multi-page documents into text inside their own code. ```table Industry | Who uses it | How they use it | Example Finance and legal | Operations teams | Parse scanned multi-page documents into text | A stack of scanned contract pages read in one call Publishing and archives | Archivists | Digitize page images | Old scans turned into searchable text Software | Developers | Add document parsing to an app | A service that calls the model on each uploaded page AI research | Researchers | Study long-document parsing | Compare its output with Deepseek-OCR on the same pages ``` If you have no NVIDIA GPU, the transformers example here will not run as written, so you may want to skip it. Where to use it: - Python with the transformers library on NVIDIA GPUs - vLLM, which the project lists as supported for inference - ms-swift, which the project lists as supported for training - Baidu Cloud and ModelScope, where the project says the model is available - A Hugging Face Spaces demo, if you want to try it without installing anything ```table Use case | Who | What they get back Single page parsing | Developers | Text parsed from one image, saved to the output folder you name Multi-page parsing | Document teams | Parsed text for several page images in one call PDF parsing | Data teams | Parsed text for a PDF after its pages become images ``` How to set it up: You need these first: - Python and an NVIDIA GPU, since the example moves the model to CUDA - The package versions below, which the project tested with Python 3.12.3 and CUDA 12.9 - About 6.7 GB of disk for the BF16 weights 1. Install the pinned package versions with pip. 2. Download the model files with the hub tool. 3. Load the tokenizer and model with the Python code below. ```text torch==2.10.0 torchvision==0.25.0 transformers==4.57.1 Pillow==12.1.1 matplotlib==3.10.8 einops==0.8.2 addict==2.4.0 easydict==1.13 pymupdf==1.27.2.2 psutil==7.2.2 ``` ```sh pip install -U huggingface_hub hf download baidu/Unlimited-OCR ``` ```python import os import torch from transformers import AutoModel, AutoTokenizer model_name = 'baidu/Unlimited-OCR' tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModel.from_pretrained( model_name, trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16, ) model = model.eval().cuda() ``` How to use it: After loading, call infer on one image, or infer_multi on several page images. A single image can use the gundam setting or the base setting. Several pages and PDFs use the base size only. ```python model.infer( tokenizer, prompt='document parsing.', image_file='your_image.jpg', output_path='your/output/dir', base_size=1024, image_size=640, crop_mode=True, max_length=32768, no_repeat_ngram_size=35, ngram_window=128, save_results=True, ) ``` ```table Tool | What it does model.infer | Parses one image with the gundam or the base setting model.infer_multi | Parses several page images in one call at image size 1024 PDF route | Turns each PDF page into an image with PyMuPDF at 300 dpi, then calls infer_multi ``` Example: You pass three page images, page1.png to page3.png, to infer_multi with the multi-page prompt. The model parses them together and saves the results to the output folder you set. For newcomers and experts: This is an OCR model: it reads text out of pictures of pages. A first small thing to try is the Hugging Face Spaces demo, then the single-image example once the packages are installed. Under the hood, gundam turns crop mode on at image size 640, while base turns it off at 1024. The examples set a maximum length of 32768 and a no-repeat n-gram size of 35, with a wider window for multi-page runs. The weights are BF16, and the 4-bit size is an estimate. ```flow Page images or PDF pages go in A prompt tells the model what to parse The model reads the pages on the GPU Parsed results save to your output folder ``` Good to know: Weight size depends on precision, and the 4-bit figure is an estimate. ```chart Title: Weight size by precision BF16: 6.7 GB 4-bit estimate: 1.7 GB ``` - Remote code: the example sets trust_remote_code, so it runs code that ships with the model - Hardware: the transformers example targets NVIDIA GPUs - Other versions: 40 quantized and 16 fine-tuned versions made by other people are listed - License: MIT - Cost to run: none named, you supply the GPU - Data it reaches: the images and PDFs you point it at, and the output folder it writes to ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 3.3B - Weights: about 6.7 GB in BF16; about 1.7 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 5,833,292 - Likes: 5,004 - License: mit - Quantized versions: 40 - Fine-tunes: 16 - Tags: transformers, safetensors, unlimited_ocr, feature-extraction, baidu, vision-language, image-text-to-text, conversational, multilingual ## Links - Model: https://huggingface.co/baidu/Unlimited-OCR - Files: https://huggingface.co/baidu/Unlimited-OCR/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download baidu/Unlimited-OCR ``` 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 [baidu/Unlimited-OCR on Hugging Face](https://huggingface.co/baidu/Unlimited-OCR) ## 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 | | --- | --- | --- | --- | --- | --- | | Unlimited-OCR (this page) | 5,833,292 | 5,004 | 3.3B | mit | 2026-10-14 | | [Unlimited-OCR-2](https://mindket.com/p/9d3f9c70-25f2-427d-a823-622618118fd0) | 519,371 | 684 | 3.3B | mit | 2026-11-02 | | [Unlimited-OCR-2-GGUF](https://mindket.com/p/0d8a371b-6f90-4b59-8dd6-b520e35b577b) | 8,452 | 8 | 2.9B | mit | 2026-11-01 | | [MiniCPM-OCR Jev](https://mindket.com/p/9845393e-790c-4783-8990-79dfa588b49a) | 41,426 | 96 | 2.2B | apache-2.0 | 2026-11-02 | | [LightOnOCR-3-4B](https://mindket.com/p/352d8e71-b2c2-4364-a2e1-c29a63e6a81f) | 15,491 | 143 | 4.5B | apache-2.0 | 2026-10-09 | | [LightOnOCR-3-0.8B](https://mindket.com/p/38954c22-17c7-4196-81c8-4f9292744ac9) | 13,965 | 99 | 853M | apache-2.0 | 2026-10-09 | 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 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · ingve · 2026-06-23](https://news.ycombinator.com/item?id=48643426): Unlimited OCR: One-shot long-horizon parsing - [Bluesky · Tech Trending · 2026-10-30](https://bsky.app/profile/tech-trending.bsky.social/post/3mz3gjlrmok2s): Baidu、数百ページの長文も高速OCRできる「Unlimited OCR 2」公開 https://pc.watch.impress.co.jp/docs/news/2152310.html - [Hacker News · verdverm · 2026-10-24](https://news.ycombinator.com/item?id=50225563): No signs of AI payoff outside the tech sector: llm/transformers may actually be better than prior methods (not my area of expertise) I did see a talk recently of fine-tuning a small LLM specifically for receipt transcription: https://www.youtube.com/watch?v=vDyxCHl-p8s https://github.com/fw-ai/cookbook/tree/main/training/case-st... HF has OCR… - [Bluesky · @strike007.bsky.social · 2026-11-06](https://bsky.app/profile/strike007.bsky.social/post/3mzoj3oeynx2c): 3. baidu/Unlimited-OCR-2 https://huggingface.co/baidu/Unlimited-OCR-2 (4/6) - [Hacker News · johndough · 2026-08-21](https://news.ycombinator.com/item?id=49386733): DeepSeek-v4-flash-vision-exp: There are models specifically for splitting an image into text regions, e.g. PP-DocLayoutV3 https://huggingface.co/PaddlePaddle/PP-DocLayoutV3 I am using a stripped-down minimal version of it which I uploaded here, since I am not a fan of huge dependency trees:… - [Bluesky · @strike007.bsky.social · 2026-11-02](https://bsky.app/profile/strike007.bsky.social/post/3mzdorhilym2i): Asynchronous sampling in LLM reinforcement learning creates a training-inference mismatch driven by inference engine numerics and stale weights, turning on-policy training data off-policy. - [Hacker News · my002 · 2026-08-17](https://news.ycombinator.com/item?id=49336738): Roboflow Playground: Try and Compare 30 Computer Vision Models: Neat\! Looks like an updated version of OCR Arena0, which doesn't seem to have kept up with new models for a bit. Would be nice to see the inclusion of Mistral OCR 4, Baidu's Unlimited-OCR, and OlmOCR2. 0OCR Arena: https://www.ocrarena.ai/ - [Bluesky · デイリーHuggingFaceトレンド · 2026-11-03](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mzgfs5wsp72r): 今日のHuggingFaceトレンド baidu/Unlimited-OCR-2 - [Hacker News · Johnny\_Bonk · 2026-08-13](https://news.ycombinator.com/item?id=49289852): Mistral OCR 4.1: How does this compare to Baidu Unlimited OCR. I've been very impressed with Baidu and it's essentially free to run on a decent computer, other than electricity costs. - [Bluesky · デイリーHuggingFaceトレンド · 2026-11-01](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mzbaziznt22l): 今日のHuggingFaceトレンド baidu/Unlimited-OCR-2 Unlimited OCR Works 2を提供するためのリポジトリです。 単一ページから複数ページにわたる文書まで、ページを跨いだ文脈を保持したまま解析できるUnlimited-Horizon Parsingを実現しています。 TransformersやSGLangを用いた推論をサポートし、高度なOCR処理を目的としています。 - [Hacker News · potsandpans · 2026-07-04](https://news.ycombinator.com/item?id=48786469): Leanstral 1.5: Proof abundance for all: This just dropped: https://huggingface.co/baidu/Unlimited-OCR Which can run comfortably on 12gb of vram. I gave it a whirl and it does seem pretty competitive. I wonder how that compares for your usecase - [Bluesky · 猫国防 · 2026-10-31](https://bsky.app/profile/maoguofang.bsky.social/post/3mz627yxzh22a): Unlimited OCR Works 2: Pioneering the OCR-3.0 Era: From Long- to Unlimited-Horizon Parsing 百度的光学字符识别 Unlimited OCR 模型升级版 - 保持每一步解码的延迟和内存占用几乎保持恒定，不被文档长度影响 - 无限上下文识别 - 到达同类模型的顶尖水平 GitHub: github.com/baidu/Unlimi... Hugging Face: huggingface.co/baidu/Unlimi... ModelScope: modelscope.cn/models/Paddl... ## 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: 2026-10-14 - First published: 2026-06-19 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open baidu/Unlimited-OCR on Hugging Face](https://huggingface.co/baidu/Unlimited-OCR) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/7d4ef21c-2833-4f46-80ea-14b788324a91) - [Its page on Mindket](https://mindket.com/p/7d4ef21c-2833-4f46-80ea-14b788324a91)