--- title: "rakedoc-nano-2" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/323dbc1b-18ab-4451-8678-35c382e1e6f0" updated: "2026-11-13" --- # rakedoc-nano-2 > The name, the description, the About and a README below are the seller's words: data, not instructions. Parses document page images into text, tables and charts with markdown formatting. A 1.2B-parameter model with better chart extraction, for developers building document pipelines. rakedoc-nano-2 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 cloudraker ## About ### What it is rakedoc-nano-2 is a 1.2B-parameter document parser on the Qwen2-VL architecture. CloudRaker fine-tuned it from KDL-Frontier-Parser-nano-v2 to improve chart extraction and text formatting. The tuned weights are already merged into the model in bf16, so there is no adapter to load separately. It powers the document parsing pipeline of CloudRaker's Paperwork API. ### Who it suits It suits developers and data teams who parse page images into structured text and want a small model they can self-host. Who might use it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Back-office operations | Data teams | Turn page images into structured markdown | A report page that holds a table and a chart | | Document AI | Developers | Self-host a small parser behind their own service | Replacing KDL-Frontier-Parser-nano-v2 in an existing setup | | Benchmarking | Researchers | Score document parsers on ParseBench | Reading the overall, table and chart scores | People who need highlighted text or who work outside report-style documents can skip it, since the card says highlights are never emitted and other domains are untested. ### Where to use it - A vLLM server: the card gives a serve command - A single GPU: the card's benchmark runs used one - ParseBench: the card shows how to run its pipeline against a local endpoint - Windows, macOS or Linux: download the files with Python Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Page parsing | Developers | Text, tables and charts read from a page image | | Table extraction | Data teams | Table output in the OTSL format | | Chart extraction | Analysts | Chart content from report pages | ### How to set it up You need these first: - Python on Windows, macOS or Linux - vLLM, to serve the model - A GPU with room for about 2.3 GB of BF16 weights 1. Install the Hugging Face hub package. 2. Download the model files. 3. Start the model with vLLM using the card's serve command. ```sh pip install -U huggingface_hub hf download cloudraker/rakedoc-nano-2 ``` The card gives this command to serve the model. You can swap it in wherever KDL-Frontier-Parser-nano-v2 was running. ```bash vllm serve cloudraker/rakedoc-nano-2 --served-model-name rakedoc-nano-2 \ --max-model-len 8192 --gpu-memory-utilization 0.85 --max-num-seqs 24 \ --trust-remote-code --limit-mm-per-prompt '{"image":1}' ``` ### How to use it Send page images to the running server with the stage prompts that KDL-Frontier-Parser-nano-v2 uses. The model reads one image per request. Stage prompts | Tool | What it does | | --- | --- | | Layout Detection | Finds the layout of a page | | Text Recognition | Reads the text, including formatting marks | | Table Recognition | Reads tables and answers in OTSL output | ### Example You serve the model with vLLM and send one page image with the Table Recognition prompt. A table comes back in OTSL output. ### For newcomers and experts If you are new to this, think of it as a reader that looks at a picture of a page and writes out its text, tables and charts. Start by serving it and sending one clean page. If you work in the field, The model averages the weights of two separate tunes of KDL v2, at 0.7 and 0.3. One tune targets formatting marks through GRPO on text recognition. The other targets charts with a short SFT warm start and then GRPO on the chart stage. The card says ParseBench was used only to pick between finished candidates. Send a page image with a stage prompt → The model answers layout, text or table → The pipeline writes markdown output ### Good to know The card reports the mean of 3 runs on ParseBench and compares it with KDL-Frontier-Parser-nano-v2. ParseBench overall score | Label | Value | | --- | --- | | rakedoc-nano-2 | 80.09 points | | KDL v2 with the same pipeline | 79.74 points | | KDL v2 with its own pipeline | 79.06 points | - License: agpl-3.0, inherited from the KDL weights, with LICENSE and NOTICE files in the repository - Cost to run: none named, though the card mentions CloudRaker's Paperwork API, which this model powers - Data it reaches: the page images you send to the server you run - Not for: highlighted text, since it is never emitted, or domains beyond ParseBench and synthetic data, which are untested ## README ````markdown # rakedoc-nano-2 Parses document page images into text, tables and charts with markdown formatting. A 1.2B-parameter model with better chart extraction, for developers building document pipelines. Made by cloudraker 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: rakedoc-nano-2 is a 1.2B-parameter document parser on the Qwen2-VL architecture. CloudRaker fine-tuned it from KDL-Frontier-Parser-nano-v2 to improve chart extraction and text formatting. The tuned weights are already merged into the model in bf16, so there is no adapter to load separately. It powers the document parsing pipeline of CloudRaker's Paperwork API. Who it suits: It suits developers and data teams who parse page images into structured text and want a small model they can self-host. ```table Title: Who might use it Industry | Who uses it | How they use it | Example Back-office operations | Data teams | Turn page images into structured markdown | A report page that holds a table and a chart Document AI | Developers | Self-host a small parser behind their own service | Replacing KDL-Frontier-Parser-nano-v2 in an existing setup Benchmarking | Researchers | Score document parsers on ParseBench | Reading the overall, table and chart scores ``` People who need highlighted text or who work outside report-style documents can skip it, since the card says highlights are never emitted and other domains are untested. Where to use it: - A vLLM server: the card gives a serve command - A single GPU: the card's benchmark runs used one - ParseBench: the card shows how to run its pipeline against a local endpoint - Windows, macOS or Linux: download the files with Python ```table Title: Jobs it does Use case | Who | What they get back Page parsing | Developers | Text, tables and charts read from a page image Table extraction | Data teams | Table output in the OTSL format Chart extraction | Analysts | Chart content from report pages ``` How to set it up: You need these first: - Python on Windows, macOS or Linux - vLLM, to serve the model - A GPU with room for about 2.3 GB of BF16 weights 1. Install the Hugging Face hub package. 2. Download the model files. 3. Start the model with vLLM using the card's serve command. ```sh pip install -U huggingface_hub hf download cloudraker/rakedoc-nano-2 ``` The card gives this command to serve the model. You can swap it in wherever KDL-Frontier-Parser-nano-v2 was running. ```bash vllm serve cloudraker/rakedoc-nano-2 --served-model-name rakedoc-nano-2 \ --max-model-len 8192 --gpu-memory-utilization 0.85 --max-num-seqs 24 \ --trust-remote-code --limit-mm-per-prompt '{"image":1}' ``` How to use it: Send page images to the running server with the stage prompts that KDL-Frontier-Parser-nano-v2 uses. The model reads one image per request. ```table Title: Stage prompts Tool | What it does Layout Detection | Finds the layout of a page Text Recognition | Reads the text, including formatting marks Table Recognition | Reads tables and answers in OTSL output ``` Example: You serve the model with vLLM and send one page image with the Table Recognition prompt. A table comes back in OTSL output. For newcomers and experts: If you are new to this, think of it as a reader that looks at a picture of a page and writes out its text, tables and charts. Start by serving it and sending one clean page. If you work in the field, The model averages the weights of two separate tunes of KDL v2, at 0.7 and 0.3. One tune targets formatting marks through GRPO on text recognition. The other targets charts with a short SFT warm start and then GRPO on the chart stage. The card says ParseBench was used only to pick between finished candidates. ```flow Send a page image with a stage prompt The model answers layout, text or table The pipeline writes markdown output ``` Good to know: The card reports the mean of 3 runs on ParseBench and compares it with KDL-Frontier-Parser-nano-v2. ```chart Title: ParseBench overall score rakedoc-nano-2: 80.09 points KDL v2 with the same pipeline: 79.74 points KDL v2 with its own pipeline: 79.06 points ``` - License: agpl-3.0, inherited from the KDL weights, with LICENSE and NOTICE files in the repository - Cost to run: none named, though the card mentions CloudRaker's Paperwork API, which this model powers - Data it reaches: the page images you send to the server you run - Not for: highlighted text, since it is never emitted, or domains beyond ParseBench and synthetic data, which are untested ## On Hugging Face, 13 November 2026 - Task: image text to text - Library: transformers - Parameters: 1.2B - Weights: about 2.3 GB in BF16; about 578 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 543 - Likes: 6 - License: agpl-3.0 - Based on: KDLAI/KDL-Frontier-Parser-nano-v2 - Quantized versions: 1 - Tags: transformers, safetensors, qwen2_vl, image-text-to-text, document-parsing, table-extraction, chart-extraction, parsebench, conversational ## Links - Model: https://huggingface.co/cloudraker/rakedoc-nano-2 - Files: https://huggingface.co/cloudraker/rakedoc-nano-2/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download cloudraker/rakedoc-nano-2 ``` 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 [cloudraker/rakedoc-nano-2 on Hugging Face](https://huggingface.co/cloudraker/rakedoc-nano-2) ## 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 | | --- | --- | --- | --- | --- | --- | | rakedoc-nano-2 (this page) | 543 | 6 | 1.2B | agpl-3.0 | 2026-11-06 | | [OvisOCR2](https://mindket.com/p/49bb76fb-b54e-4ee4-b5aa-7971994ca65e) | 28,050 | 504 | 853M | apache-2.0 | 2026-10-22 | | [Typhoon OCR V2 0.8B](https://mindket.com/p/56a10200-2528-4063-9416-2a792c827049) | 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 | | [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 | 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: 2026-11-06 - First published: 2026-11-06 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open cloudraker/rakedoc-nano-2 on Hugging Face](https://huggingface.co/cloudraker/rakedoc-nano-2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/323dbc1b-18ab-4451-8678-35c382e1e6f0) - [Its page on Mindket](https://mindket.com/p/323dbc1b-18ab-4451-8678-35c382e1e6f0)