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  # Reasoning comes to OCR 🧠✨📄🤘
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  **NuMarkdown-8B-Thinking** is the first reasoning OCR VLM. It is specifically trained to convert documents into clean GitHub-flavoured Markdown. It generates thoughts tokens to figure out the layout of the document before generating the Markdown file.
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- It is particularly good at understanding documents with weird layout and complex tables. The number of thinking tokens can vary from 20% to 500% of the number of tokens for the final answer, depending on the task difficulty.
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- **NuMarkdown-8B-Thinking** is a fine-tune of **Qwen 2.5-VL-7B** using synthetic Doc → Reasoning → Markdown examples, followed by an RL phase (GRPO) with a layout-centric reward.
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  ## Results
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- **NuMarkdown-8B-Thinking** is outperforming generic non-reasoning models like GPT-4o and specialized markdown generation models like OCRFlux.
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  It is competitive against large reasoning closed-source models like Gemini 2.5.
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  ### Arena ranking against popular alternatives (using trueskill-2 ranking system, with around 500 model-anonymized votes):
 
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  # Reasoning comes to OCR 🧠✨📄🤘
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  **NuMarkdown-8B-Thinking** is the first reasoning OCR VLM. It is specifically trained to convert documents into clean GitHub-flavoured Markdown. It generates thoughts tokens to figure out the layout of the document before generating the Markdown file.
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+ 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.
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+ **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.
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  ## Results
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+ **NuMarkdown-8B-Thinking** is outperforming generic non-reasoning models like GPT-4o and specialized OCR models like OCRFlux.
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  It is competitive against large reasoning closed-source models like Gemini 2.5.
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  ### Arena ranking against popular alternatives (using trueskill-2 ranking system, with around 500 model-anonymized votes):