--- title: "Typhoon OCR V2 0.8B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/56a10200-2528-4063-9416-2a792c827049" updated: "2026-11-12" --- # Typhoon OCR V2 0.8B > The name, the description, the About and a README below are the seller's words: data, not instructions. Parses Thai and English document pages into ordered layout elements with categories, boxes and text, using 0.8B parameters. For developers who digitize Thai paperwork. Typhoon OCR V2 0.8B 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 typhoon-ai ## About ### What it is Typhoon OCR V2 0.8B is a document parser for Thai and English with 0.8B parameters. Send it a picture of one page and it lists every element it finds, ordered the way a person would read the page. Each entry carries a type label, a box that marks where it sits and the words inside. Table content arrives as HTML markup, math as LaTeX and the remaining text as Markdown. ### Who it suits Developers and teams who turn Thai or English page images into structured data and want a small model to do it. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Government | Records offices | Digitize Thai forms and letters | A scanned form becomes ordered text with boxes | | Finance | Back-office teams | Pull tables out of statements | A statement table comes back as HTML | | Research | Academics | Extract text and formulas from papers | A formula comes back as LaTeX | | Software | Developers | Add page parsing to an app | An app reads a photographed page | You can skip it if you need to answer questions about a document. The card sends you to the larger 2B version for that. ### Where to use it - Hugging Face Transformers, which the card says is unaffected by the vLLM issue - vLLM, version 0.30.0 or newer - Ollama, llama.cpp and MLX, through other builds the card lists - The hosted Typhoon API, as the model typhoon-ocr-v2 | Use case | Who | What they get back | | --- | --- | --- | | Full-page parsing | Developers | Layout elements in reading order with categories and boxes | | Table extraction | Analysts | Tables as HTML | | Formula extraction | Researchers | Formulas as LaTeX | ### How to set it up - Python and pip on your computer - vLLM 0.30.0 or newer, or Transformers, to run the model - Optionally the typhoon-ocr2 Python package 1. Download the model files with the hf command. 2. Install vLLM 0.30.0 or newer. On an older vLLM, run in eager mode. 3. Follow the usage section of the model card to send it a page image. ```sh pip install -U huggingface_hub hf download typhoon-ai/typhoon-ocr2-0.8b ``` ```sh pip install typhoon-ocr2 ``` ### How to use it Send the model a page image and read back the structured elements. With an older vLLM, add the eager flag when you serve the model. The card also lists an Ollama build. | Tool | What it does | | --- | --- | | vllm serve with --enforce-eager | Runs the model on older vLLM versions at full accuracy | | typhoon-ocr2 package | Python helper for calling the model | | ollama run SuraponOCR/typhoon-ocr2-0.8b | Runs the model through Ollama | ```sh ollama run SuraponOCR/typhoon-ocr2-0.8b ``` ### Example You send a photo of a Thai invoice page. The model returns a list of elements, such as a title, text blocks and a table. Each has a category and a box, and the table text is in HTML. ### For newcomers and experts If you are new, try it on one clean page image and read the output in order. Use it only for whole-page parsing, since the card says it is much weaker on other tasks. If you know the field, it is a fine-tune of OvisOCR2 trained with supervised fine-tuning and then GRPO, with rewards that give zero to runaway outputs. On MDPBench the card reports 77.4 against 66.0 for Typhoon OCR 1.5. Some English and Chinese accuracy was lost against the base model. Page image → Typhoon OCR V2 0.8B → Elements in reading order → Boxes, categories and text MDPBench score | Label | Value | | --- | --- | | Typhoon OCR 1.5 | 66.0 | | Typhoon OCR V2 0.8B | 77.4 | ### Good to know - License: Apache 2.0, and the card says use is also subject to the OpenTyphoon Terms and Conditions - Cost to run: your own hardware, or the hosted Typhoon API, whose price the card does not give - Data it reaches: the page images you give it and the files you download - Not for: answering questions about documents, which the card assigns to the 2B model - Careful: older vLLM versions in compiled mode lowered accuracy in the card's tests ## README ````markdown # Typhoon OCR V2 0.8B Parses Thai and English document pages into ordered layout elements with categories, boxes and text, using 0.8B parameters. For developers who digitize Thai paperwork. Made by typhoon-ai 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: Typhoon OCR V2 0.8B is a document parser for Thai and English with 0.8B parameters. Send it a picture of one page and it lists every element it finds, ordered the way a person would read the page. Each entry carries a type label, a box that marks where it sits and the words inside. Table content arrives as HTML markup, math as LaTeX and the remaining text as Markdown. Who it suits: Developers and teams who turn Thai or English page images into structured data and want a small model to do it. ```table Industry | Who uses it | How they use it | Example Government | Records offices | Digitize Thai forms and letters | A scanned form becomes ordered text with boxes Finance | Back-office teams | Pull tables out of statements | A statement table comes back as HTML Research | Academics | Extract text and formulas from papers | A formula comes back as LaTeX Software | Developers | Add page parsing to an app | An app reads a photographed page ``` You can skip it if you need to answer questions about a document. The card sends you to the larger 2B version for that. Where to use it: - Hugging Face Transformers, which the card says is unaffected by the vLLM issue - vLLM, version 0.30.0 or newer - Ollama, llama.cpp and MLX, through other builds the card lists - The hosted Typhoon API, as the model typhoon-ocr-v2 ```table Use case | Who | What they get back Full-page parsing | Developers | Layout elements in reading order with categories and boxes Table extraction | Analysts | Tables as HTML Formula extraction | Researchers | Formulas as LaTeX ``` How to set it up: - Python and pip on your computer - vLLM 0.30.0 or newer, or Transformers, to run the model - Optionally the typhoon-ocr2 Python package 1. Download the model files with the hf command. 2. Install vLLM 0.30.0 or newer. On an older vLLM, run in eager mode. 3. Follow the usage section of the model card to send it a page image. ```sh pip install -U huggingface_hub hf download typhoon-ai/typhoon-ocr2-0.8b ``` ```sh pip install typhoon-ocr2 ``` How to use it: Send the model a page image and read back the structured elements. With an older vLLM, add the eager flag when you serve the model. The card also lists an Ollama build. ```table Tool | What it does vllm serve with --enforce-eager | Runs the model on older vLLM versions at full accuracy typhoon-ocr2 package | Python helper for calling the model ollama run SuraponOCR/typhoon-ocr2-0.8b | Runs the model through Ollama ``` ```sh ollama run SuraponOCR/typhoon-ocr2-0.8b ``` Example: You send a photo of a Thai invoice page. The model returns a list of elements, such as a title, text blocks and a table. Each has a category and a box, and the table text is in HTML. For newcomers and experts: If you are new, try it on one clean page image and read the output in order. Use it only for whole-page parsing, since the card says it is much weaker on other tasks. If you know the field, it is a fine-tune of OvisOCR2 trained with supervised fine-tuning and then GRPO, with rewards that give zero to runaway outputs. On MDPBench the card reports 77.4 against 66.0 for Typhoon OCR 1.5. Some English and Chinese accuracy was lost against the base model. ```flow Page image Typhoon OCR V2 0.8B Elements in reading order Boxes, categories and text ``` ```chart Title: MDPBench score Typhoon OCR 1.5: 66.0 Typhoon OCR V2 0.8B: 77.4 ``` Good to know: - License: Apache 2.0, and the card says use is also subject to the OpenTyphoon Terms and Conditions - Cost to run: your own hardware, or the hosted Typhoon API, whose price the card does not give - Data it reaches: the page images you give it and the files you download - Not for: answering questions about documents, which the card assigns to the 2B model - Careful: older vLLM versions in compiled mode lowered accuracy in the card's tests ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 853M - Weights: about 1.7 GB in BF16; about 426 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 485 - Likes: 8 - License: apache-2.0 - Languages: th, en - Based on: ATH-MaaS/OvisOCR2 - Quantized versions: 4 - Fine-tunes: 2 - Tags: transformers, safetensors, qwen3_5, image-text-to-text, document-parsing, layout-analysis, table-recognition, thai, conversational ## Links - Model: https://huggingface.co/typhoon-ai/typhoon-ocr2-0.8b - Files: https://huggingface.co/typhoon-ai/typhoon-ocr2-0.8b/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download typhoon-ai/typhoon-ocr2-0.8b ``` 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 [typhoon-ai/typhoon-ocr2-0.8b on Hugging Face](https://huggingface.co/typhoon-ai/typhoon-ocr2-0.8b) ## 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 | | --- | --- | --- | --- | --- | --- | | Typhoon OCR V2 0.8B (this page) | 485 | 8 | 853M | apache-2.0 | 2026-11-12 | | [MiniCPM-OCR 1.0](https://mindket.com/p/7b9a0052-a548-4ce3-8d62-e47483d87119) | 128,483 | 457 | 1.3B | apache-2.0 | 2026-11-08 | | [OvisOCR2](https://mindket.com/p/49bb76fb-b54e-4ee4-b5aa-7971994ca65e) | 28,050 | 504 | 853M | apache-2.0 | 2026-10-22 | | [OvisOCR3](https://mindket.com/p/80cf6589-b844-493b-beb2-356ff41f4956) | 203,064 | 121 | 853M | apache-2.0 | 2026-11-10 | | [Xiaomi-OCR-0](https://mindket.com/p/85bd3445-61e1-429e-b23c-833b2580414d) | 29,537 | 319 | 873M | apache-2.0 | 2026-10-29 | | [rakedoc-nano-2](https://mindket.com/p/323dbc1b-18ab-4451-8678-35c382e1e6f0) | 543 | 6 | 1.2B | agpl-3.0 | 2026-11-06 | 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: 2026-11-12 - First published: 2026-11-05 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open typhoon-ai/typhoon-ocr2-0.8b on Hugging Face](https://huggingface.co/typhoon-ai/typhoon-ocr2-0.8b) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/56a10200-2528-4063-9416-2a792c827049) - [Its page on Mindket](https://mindket.com/p/56a10200-2528-4063-9416-2a792c827049)