nvidia/Nemotron-3.5-Parse

image-text-to-text model by nvidia

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Nemotron 3.5 Parse is a document image parser that produces structured, machine-readable text with semantic layout classes, bounding boxes, and reading order. It combines a C-RADIOv4-SO400M vision encoder with a 10-layer mBART decoder that consumes the visual sequence as a causal prefix. The release uses dynamic rectangular NaViT preprocessing, content-normalized coordinates, grouped-query self-attention (16 query heads and 8 key/value heads), a 72,505-token multilingual tokenizer, and chart-aware output through the token. It is intended for document understanding, information retrieval, data extraction, and multimodal data-curation workflows. Nemotron 3.5 Parse was developed by NVIDIA as a part of Nemotron. This model is ready for commercial or non-commercial use. Detailed benchmarks: ParseBench · IndicVisionBench · MOSCAR. ParseBench results use triple pass with region union on 2,078 unique pages. See pipeline details, reproduction instructions, and evaluation results. Additional OCR highlights (scores on a 0–100 scale): MOSCAR uses simple synthetic renderings of text in each language…

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  1. Nemotron 3.5 Parse
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Model overview

nvidia/Nemotron-3.5-Parse is a image-text-to-text model repository published by nvidia on Hugging Face. The source reports the transformers library.

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Model facts

Task
image-text-to-text
Library
transformers
Recent downloads (30 days)
10,678
Cumulative likes
132
Hugging Face trending score
73
Reported safetensors parameters
630,800,504
Architecture
RadioMBartPrefixForCausalLM
License
openmdw-1.1
Access
Not gated by Hugging Face
Created
2026-09-03T06:29:29.000Z
Last modified
2026-10-28T04:31:24.000Z

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