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README.md
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🚀 <a href="https://huggingface.co/spaces/infly/Infinity-Parser2-Demo">Demo</a>
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## Introduction
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We are excited to release Infinity-Parser2, our latest flagship document understanding model. We offer two distinct variants to address diverse deployment constraints: Infinity-Parser2-Pro, optimized for maximum accuracy in precision-critical tasks, achieves state-of-the-art results on olmOCR-Bench (87.6%) and ParseBench (74.3%), surpassing frontier models including DeepSeek-OCR-2, PaddleOCR-VL-1.5, and MinerU-2.5. Infinity-Parser2-Flash, engineered for low-latency inference, delivers a 3.68x speedup over our previous Infinity-Parser-7B model. With significant upgrades to both our data engine and multi-task reinforcement learning approach, the model consolidates robust multi-modal parsing capabilities into a unified architecture, unlocking brand-new zero-shot capabilities across a wide range of real-world business scenarios.
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min_pixels = 2048 # 32 * 64
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max_pixels = 16777216 # 4096 * 4096
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prompt = """
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messages = [
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🚀 <a href="https://huggingface.co/spaces/infly/Infinity-Parser2-Demo">Demo</a>
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</p>
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## News
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- [2026-05-11] Released flagship document parsing models: [Infinity-Parser2-Pro](https://huggingface.co/infly/Infinity-Parser2-Pro), [Infinity-Parser2-Flash](https://huggingface.co/infly/Infinity-Parser2-Flash), and the dataset [Infinity-Doc2-5M](https://huggingface.co/datasets/infly/Infinity-Doc2-5M). [Infinity-Parser2](Infinity-Parser2) achieves SOTA results on olmOCR-bench and ParseBench.
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## Introduction
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We are excited to release Infinity-Parser2, our latest flagship document understanding model. We offer two distinct variants to address diverse deployment constraints: Infinity-Parser2-Pro, optimized for maximum accuracy in precision-critical tasks, achieves state-of-the-art results on olmOCR-Bench (87.6%) and ParseBench (74.3%), surpassing frontier models including DeepSeek-OCR-2, PaddleOCR-VL-1.5, and MinerU-2.5. Infinity-Parser2-Flash, engineered for low-latency inference, delivers a 3.68x speedup over our previous Infinity-Parser-7B model. With significant upgrades to both our data engine and multi-task reinforcement learning approach, the model consolidates robust multi-modal parsing capabilities into a unified architecture, unlocking brand-new zero-shot capabilities across a wide range of real-world business scenarios.
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min_pixels = 2048 # 32 * 64
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max_pixels = 16777216 # 4096 * 4096
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prompt = """
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- Extract layout information from the provided PDF image.
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- For each layout element, output its bbox, category, and the text content within the bbox.
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- Bbox format: [x1, y1, x2, y2].
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- Allowed layout categories: ['header', 'title', 'text', 'figure', 'table', 'formula', 'figure_caption', 'table_caption', 'formula_caption', 'figure_footnote', 'table_footnote', 'page_footnote', 'footer'].
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- Text extraction and formatting:
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1) For 'figure', the text field must be an empty string.
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2) For 'formula', format text as LaTeX.
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3) For 'table', format text as HTML.
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4) For all other categories (e.g., text, title), format text as Markdown.
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- The output text must be exactly the original text from the image, with no translation or rewriting.
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- Sort all layout elements in human reading order.
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- Final output must be a single JSON object.
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"""
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messages = [
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