--- title: "Granite-Docling-258M" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/15106c15-a0f3-410b-ab6d-511c0643583e" updated: "2026-11-13" --- # Granite-Docling-258M > The name, the description, the About and a README below are the seller's words: data, not instructions. Converts document page images into structured text, with equations, layout and questions about page elements, in a 258M model from IBM Research. For people who build document conversion with Docling. Granite-Docling-258M 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 ibm-granite ## About ### What it is Granite Docling is a small multimodal model from IBM Research that turns document page images into structured text. It has 258M parameters and fits into the Docling document pipelines. Its vision part is siglip2-base-patch16-512, and its language part is a 165M Granite model. ### Who it suits It suits developers who need to convert scanned or digital documents into formats such as Markdown and HTML. It also suits teams that already use Docling and want a model that plugs in. Who runs it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Document-pipeline developers | Convert pages to Markdown or HTML | A converter for a folder of reports | | Research | Data teams | Turn papers with equations into clean text | Maths-heavy pages with formulas intact | | Back office | Operations teams | Read page structure from document images | Check which elements a page holds | If you only work with plain text files, you can skip it. It is for page images and documents. ### Where to use it - The Docling library, the easiest route, which downloads the model by itself - Plain transformers, vllm, onnx or mlx-vlm, which the card lists for direct inference - Windows, macOS or Linux with Python - A Mac with Apple's MPS accelerator, through a Docling option for it Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Page conversion | Developers | A page in Markdown or HTML | | Equation recognition | Researchers | Formulas detected and formatted more accurately | | Region inference | Pipeline builders | Output for a chosen region of a page | | Element questions | Analysts | Answers about which elements exist and in what order | ### How to set it up - Python on Windows, macOS or Linux - The Docling library for the easiest route, installed through pip - For plain transformers, the three prerequisite packages below - About 515 MB of space for the BF16 weights 1. Install the Hugging Face Hub tools and download the model files. 2. Or install Docling with pip, which fetches the model for you. 3. For direct use, install the three packages the card lists. ```sh pip install -U huggingface_hub hf download ibm-granite/granite-docling-258M ``` ```sh pip install torch pip install docling_core pip install transformers ``` ### How to use it With Docling, you run its command line tool and pick the vlm pipeline with the granite\_docling model. You can write HTML, Markdown or split-page HTML, and ask for a layout overlay. The card's examples use a web address, so it is left out here. Docling options in the card | Tool | What it does | | --- | --- | | --to | Picks an output format, such as html or md | | --pipeline vlm | Uses the vision-language pipeline | | --vlm-model granite\_docling | Selects this model | | --show-layout | Draws the layout on the split-page output | ```sh hf download ibm-granite/granite-docling-258M ``` ### Example You point Docling at a research paper with the granite\_docling model. It reads each page image and writes Markdown plus an HTML version, with the layout shown if you ask. The card shows this on a paper in a picture. ### For newcomers and experts If you are new, this is a small AI that reads a photo or scan of a document page and writes it out as clean text. The first small thing to try is the Docling command on one short document. If you know the field, it builds on the Idefics3 design, with the vision encoder swapped for siglip2-base-patch16-512 and a Granite 165M language model. It adds more accurate equations and tends to avoid endless output loops. The trade-off is language range: Japanese, Arabic and Chinese support is experimental. Give Docling a page image or document → The Granite Docling model reads each page → Its output converts into Markdown or HTML → You check the result against the page ### Good to know Weight size in megabytes | Label | Value | | --- | --- | | BF16 | 515 MB | | 4-bit (estimate) | 129 MB | - Language: the main language is English, with Japanese, Arabic and Chinese in an experimental state. - Modes: it can read a whole page at once or a region guided by a bounding box. - Release: IBM Research released it in September 2025. - Variants: other people have published 16 quantized and 16 fine-tuned versions. - Demo: the card links a hosted demo on Hugging Face Spaces. - License: apache-2.0. - Cost to run: no API key, account or paid service is named, and the model is small enough for modest hardware. - Data it reaches: it reads the documents you give it, and you download files from Hugging Face. - Not for: pages that need languages beyond the ones the card lists. ## README ````markdown # Granite-Docling-258M Converts document page images into structured text, with equations, layout and questions about page elements, in a 258M model from IBM Research. For people who build document conversion with Docling. Made by ibm-granite 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: Granite Docling is a small multimodal model from IBM Research that turns document page images into structured text. It has 258M parameters and fits into the Docling document pipelines. Its vision part is siglip2-base-patch16-512, and its language part is a 165M Granite model. Who it suits: It suits developers who need to convert scanned or digital documents into formats such as Markdown and HTML. It also suits teams that already use Docling and want a model that plugs in. ```table Title: Who runs it Industry | Who uses it | How they use it | Example Software | Document-pipeline developers | Convert pages to Markdown or HTML | A converter for a folder of reports Research | Data teams | Turn papers with equations into clean text | Maths-heavy pages with formulas intact Back office | Operations teams | Read page structure from document images | Check which elements a page holds ``` If you only work with plain text files, you can skip it. It is for page images and documents. Where to use it: - The Docling library, the easiest route, which downloads the model by itself - Plain transformers, vllm, onnx or mlx-vlm, which the card lists for direct inference - Windows, macOS or Linux with Python - A Mac with Apple's MPS accelerator, through a Docling option for it ```table Title: Jobs it does Use case | Who | What they get back Page conversion | Developers | A page in Markdown or HTML Equation recognition | Researchers | Formulas detected and formatted more accurately Region inference | Pipeline builders | Output for a chosen region of a page Element questions | Analysts | Answers about which elements exist and in what order ``` How to set it up: - Python on Windows, macOS or Linux - The Docling library for the easiest route, installed through pip - For plain transformers, the three prerequisite packages below - About 515 MB of space for the BF16 weights 1. Install the Hugging Face Hub tools and download the model files. 2. Or install Docling with pip, which fetches the model for you. 3. For direct use, install the three packages the card lists. ```sh pip install -U huggingface_hub hf download ibm-granite/granite-docling-258M ``` ```sh pip install torch pip install docling_core pip install transformers ``` How to use it: With Docling, you run its command line tool and pick the vlm pipeline with the granite_docling model. You can write HTML, Markdown or split-page HTML, and ask for a layout overlay. The card's examples use a web address, so it is left out here. ```table Title: Docling options in the card Tool | What it does --to | Picks an output format, such as html or md --pipeline vlm | Uses the vision-language pipeline --vlm-model granite_docling | Selects this model --show-layout | Draws the layout on the split-page output ``` ```sh hf download ibm-granite/granite-docling-258M ``` Example: You point Docling at a research paper with the granite_docling model. It reads each page image and writes Markdown plus an HTML version, with the layout shown if you ask. The card shows this on a paper in a picture. For newcomers and experts: If you are new, this is a small AI that reads a photo or scan of a document page and writes it out as clean text. The first small thing to try is the Docling command on one short document. If you know the field, it builds on the Idefics3 design, with the vision encoder swapped for siglip2-base-patch16-512 and a Granite 165M language model. It adds more accurate equations and tends to avoid endless output loops. The trade-off is language range: Japanese, Arabic and Chinese support is experimental. ```flow Give Docling a page image or document The Granite Docling model reads each page Its output converts into Markdown or HTML You check the result against the page ``` Good to know: ```chart Title: Weight size in megabytes BF16: 515 MB 4-bit (estimate): 129 MB ``` - Language: the main language is English, with Japanese, Arabic and Chinese in an experimental state. - Modes: it can read a whole page at once or a region guided by a bounding box. - Release: IBM Research released it in September 2025. - Variants: other people have published 16 quantized and 16 fine-tuned versions. - Demo: the card links a hosted demo on Hugging Face Spaces. - License: apache-2.0. - Cost to run: no API key, account or paid service is named, and the model is small enough for modest hardware. - Data it reaches: it reads the documents you give it, and you download files from Hugging Face. - Not for: pages that need languages beyond the ones the card lists. ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 258M - Weights: about 515 MB in BF16; about 129 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 245,919 - Likes: 1,295 - License: apache-2.0 - Languages: en - Quantized versions: 16 - Fine-tunes: 16 - Tags: transformers, safetensors, idefics3, image-text-to-text, text-generation, documents, code, formula, chart, layout, table, document-parse ## Links - Model: https://huggingface.co/ibm-granite/granite-docling-258M - Files: https://huggingface.co/ibm-granite/granite-docling-258M/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ibm-granite/granite-docling-258M ``` 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 [ibm-granite/granite-docling-258M on Hugging Face](https://huggingface.co/ibm-granite/granite-docling-258M) ## 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 | | --- | --- | --- | --- | --- | --- | | Granite-Docling-258M (this page) | 245,919 | 1,295 | 258M | apache-2.0 | 2025-09-23 | | [indra-ocr](https://mindket.com/p/ffb3991c-77ab-4a5e-b2ca-042edbc0b406) | 8,624 | 529 | 1.3M | openrail | 2026-10-19 | | [KAT-Coder-V2.5-Dev](https://mindket.com/p/2d2b2faf-a347-4ff6-a04d-425eab2d9986) | 16,106 | 687 | 34.7B | apache-2.0 | 2026-07-28 | | [Terminal-RSI-27B](https://mindket.com/p/b7d63976-c860-4b80-abca-f0c045cd8b2b) | 2,536 | 33 | 27.8B | apache-2.0 | 2026-10-21 | | [MiMo-V2.6-Distill-Qwen-9B](https://mindket.com/p/b09cd047-a39e-47c8-9f16-bdf39b5a2f4c) | 21,525 | 703 | 9.4B | mit | 2026-10-28 | | [Qwen3.5-4B-3D-Coder-MTP-Repacked](https://mindket.com/p/77df59cf-e130-4402-b6b8-9f9d8b7ed95a) | 6,042 | 16 | 4.7B | apache-2.0 | 2026-11-03 | 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 · mseri · 2025-09-17](https://news.ycombinator.com/item?id=45277675): Granite docling 258M: a small multimodal model for efficient document conversion - [Bluesky · troy\_phd · 2025-11-30](https://bsky.app/profile/troyhernandez.bsky.social/post/3m6v32eu5r22u): Not sure if it’s ollama supported but heard good things about this… could just be my former colleagues drinking the Kool Aid but hopefully not huggingface.co/ibm-granite/... - [Hacker News · tidbeck · 2025-10-20](https://news.ycombinator.com/item?id=45643298): DeepSeek OCR: How does this compare to https://huggingface.co/ibm-granite/granite-docling-258M in performance and how they work? ## 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: 2025-09-23 - First published: 2025-05-19 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ibm-granite/granite-docling-258M on Hugging Face](https://huggingface.co/ibm-granite/granite-docling-258M) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/15106c15-a0f3-410b-ab6d-511c0643583e) - [Its page on Mindket](https://mindket.com/p/15106c15-a0f3-410b-ab6d-511c0643583e)