# NuMarkdown-8B-Thinking by NuMind: Open-Weight Model Source: https://savrn.com/models/numarkdown-8b-thinking Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve NuMarkdown-8B-Thinking (8.3B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 16.6 GB | 19.9 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 8.3 GB | 10.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 4.1 GB | 5.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 22, 2026. [NuMarkdown-8B-Thinking on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/numarkdown-8b-thinking/gpus) ## SAVRN's Notes on NuMarkdown-8B-Thinking Budget the thinking tokens before you budget anything else. NuMind's model reads a document image, works out the layout, then writes Markdown, and the publisher puts the reasoning at 20 to 500 percent of the final output depending on the page. That number, not the 8.3 billion parameters, sets throughput for a scanning pipeline. Hardware is easy: 19.9 GB in 16-bit, 10.0 GB in 8-bit, 5.0 GB in 4-bit, and the cheapest listed setup is one MI300X with 192 GB at $1.85 an hour, which leaves most of the card for batching pages. It is MIT, commercial use and redistribution included with the notices kept, and it is a fine-tune of Qwen/Qwen2.5-VL-7B-Instruct, so a buyer should read the base model's terms alongside. The 128,000-token context comes with a 32,768-token sliding window, which matters for long documents. It was released July 29, 2025 and last updated June 5, 2026. SAVRN Research, 2026-09-18 ## Model Card By NuMind, published under mit, revision 8dfc5236ae68. [API / Platform](https://nuextract.ai/) | [Discord](https://discord.gg/3tsEtJNCDe) | [GitHub](https://github.com/numindai/NuMarkdown) | [Demo](https://huggingface.co/spaces/numind/NuMarkdown-8b-Thinking) ### Reasoning comes to OCR NuMarkdown-8B-Thinking is the first reasoning OCR VLM. It is specifically trained to convert documents into clean Markdown files, well suited for RAG applications. It generates thinking tokens to figure out the layout of the document before generating the Markdown file. It is particularly good at understanding documents with weird layouts and complex tables. The number of thinking tokens can vary from 20% to 500% of the final answer, depending on the task difficulty. NuMarkdown-8B-Thinking is a fine-tune of Qwen 2.5-VL-7B on synthetic Doc → Reasoning → Markdown examples, followed by an RL phase (GRPO) with a layout-centric reward. Try it out in [the space!](https://huggingface.co/spaces/numind/NuMarkdown-8b-Thinking) ### Results NuMarkdown-8B-Thinking is outperforming generic non-reasoning models like GPT-4o and specialized OCR models like OCRFlux. It is competitive against large reasoning closed-source models like Gemini 2.5. #### Arena ranking against popular alternatives (using trueskill-2 ranking system, with around 500 model-anonymized votes): [Read the full model card (1,408 words)](https://savrn.com/models/numarkdown-8b-thinking/card) ## Configuration Architecture Qwen2_5_VLForConditionalGeneration Context length (tokens) 128,000 Layers 28 Hidden size 3,584 Feed-forward size 18,944 Attention heads 28 Key/value heads 4 Vocabulary size 152,064 Sliding window (tokens) 32,768 RoPE base 1e+06 Stored precision bfloat16 Model type qwen2_5_vl ## Identity and Version Repository numind/NuMarkdown-8B-Thinking Publisher NuMind Task Image to text Modality Image and text Library transformers Parameters 8.3B parameters Languages Not stated by the source Revision 8dfc5236ae68b799d86308aae2f9717fdb50286e First published 2025-07-29 Last updated 2026-06-05 ## Files and Weights 21 files, 16.6 GB in total. The weights are 4 files totalling 16.6 GB in safetensors. Weights4 files · 16.6 GB Configuration7 files · 62.2 KB Tokenizer4 files · 15.9 MB Documentation1 file · 11.6 KB Other4 files · 321.6 KB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model-00001-of-00004.safetensors | Weights | 5.0 GB | de84fb5ed350 | | model-00002-of-00004.safetensors | Weights | 5.0 GB | 285127f9ef61 | | model-00003-of-00004.safetensors | Weights | 4.9 GB | e9c86dc639da | | model-00004-of-00004.safetensors | Weights | 1.7 GB | dd6206837057 | | added_tokens.json | Configuration | 605 B | — | | chat_template.json | Configuration | 1.0 KB | — | | config.json | Configuration | 1.5 KB | — | | generation_config.json | Configuration | 282 B | — | | model.safetensors.index.json | Configuration | 57.6 KB | — | | preprocessor_config.json | Configuration | 576 B | — | | special_tokens_map.json | Configuration | 613 B | — | | README.md | Documentation | 11.6 KB | — | | bar plot.png | Other | 85.0 KB | — | | ex1.png | Other | 163.3 KB | 9ab65794a94e | | matrix.png | Other | 63.4 KB | — | | numind.svg | Other | 9.9 KB | — | | .gitattributes | Repository | 1.6 KB | — | | merges.txt | Tokenizer | 1.7 MB | — | | tokenizer.json | Tokenizer | 11.4 MB | 9c5ae00e602b | | tokenizer_config.json | Tokenizer | 5.9 KB | — | | vocab.json | Tokenizer | 2.8 MB | — | ## License and Download License mit Access Open weights, no gate Download size 16.6 GB [Download from NuMind](https://huggingface.co/numind/NuMarkdown-8B-Thinking) Released by NuMind through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Derived from [Qwen/Qwen2.5-VL-7B-Instruct](https://savrn.com/models/qwen2-5-vl-7b-instruct) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 16.6 GB | | 16-bit | 16.6 GB | | 8-bit | 8.3 GB | | 4-bit | 4.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About NuMarkdown-8B-Thinking ### How much GPU memory does NuMarkdown-8B-Thinking need? About 19.9 GB at 16-bit and 5 GB at 4-bit: the weights (8.3B parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run NuMarkdown-8B-Thinking on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use NuMarkdown-8B-Thinking commercially? Yes. NuMarkdown-8B-Thinking is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included. ### What is NuMarkdown-8B-Thinking's context length? 128,000 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Image to text ### [Qwen3-VL-8B-Instruct-NVFP4](https://savrn.com/models/qwen3-vl-8b-instruct-nvfp4) [LEE, HYEOKSEUNG](https://savrn.com/model-publishers/jeildlwlrma) This is an NVFP4 quantized version of Qwen3-VL-8B-Instruct, a powerful vision-language model for multimodal understanding and generation tasks. The following modules were excluded from quantization to maintain model quality: - lmhead (language model head) - Visual encoder modules (model.visual.) - MLP gate projections (.mlp.gate$) For faster inference, you can use this model with vLLM: This quantized model maintains high quality for vision-language tasks while significantly reducing memory usage. The SmoothQuant technique helps preserve model accuracy during quantization. Typical quality degradation is 2-5% compared to the full-precision model. 1. Calibration: Used 512 samples from the… Open weights apache-2.0 5.7B parameters 262,144 tokens [View model](https://savrn.com/models/qwen3-vl-8b-instruct-nvfp4) Model · Image to text ### [NuExtract3](https://savrn.com/models/nuextract3) [NuMind](https://savrn.com/model-publishers/numind) NuExtract3 is a unified 4B vision-language reasoning model for document understanding. It combines strong structured information extraction with high-quality image-to-Markdown conversion, making it suitable for extraction pipelines, OCR, and RAG preprocessing for all types of documents such as scans, receipts, forms, invoices, contracts or tables. Try it out in the space! - Multilingual documents. - Reasoning and non-reasoning inference modes. - Template generation for structured extraction from natural language or input document. 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Combined with a two-stage pipeline of layout analysis and parallel recognition based on PP-DocLayout-V3, GLM-OCR delivers robust and high-quality OCR performance… Open weights mit 1.3B parameters 131,072 tokens transformers [View model](https://savrn.com/models/glm-ocr-2) ## NuMind [All models and datasets](https://savrn.com/model-publishers/numind) ## Versions - [8dfc5236ae68](https://savrn.com/models/numarkdown-8b-thinking/versions/8dfc5236ae68) · current 2026-09-18 ## Explore More - [All image to text models](https://savrn.com/models/tasks/image-to-text) - [All models under mit](https://savrn.com/models/licenses/mit) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-20. - [Hugging Face record](https://huggingface.co/numind/NuMarkdown-8B-Thinking) - [How the hub is built](https://savrn.com/model-hub/methodology)