Model reference · open weights

nougat

LLMs facebook Image→text 1 build Non-commercial 87k dl/mo

nougat is an open-weight language model from Meta. nougat-base (FP32) weighs 698 MB; the smallest configuration that runs it is RTX 3060 12 GB.

  • Nougat is a 349M parameter image-to-text model developed by Meta for transcribing scientific PDFs into Markdown.
  • It uses a Swin Transformer vision encoder and an mBART text decoder to predict text from image pixels.
  • The model is released under the cc-by-nc-4.0 licence.

Summary of the facebook/nougat-base model card, 2026-10-01

What it is

Released byMeta
Released2023-09-21
Parameters349M
VRAM698 MB for the weights

What it runs on

Memory and cards for nougat-base (FP32)

698 MBweights, file size
762 MBruntime overhead, at least

How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.

CardWeights alone
RTX 3060 12 GBfits
RTX 4060 Ti 16 GBfits
RTX 3090 24 GBfits
RTX 4090 24 GBfits
RTX 5090 32 GBfits
L40S 48 GBfits
A100 80 GBfits
H100 80 GBfits
RTX PRO 6000 Blackwell 96 GBfits
DGX Spark (GB10) 128 GB unifiedfits
H200 141 GBfits
B200 180 GBfits

From the model card

What Meta says about nougat

Read the model card

Nougat model trained on PDF-to-markdown. It was introduced in the paper Nougat: Neural Optical Understanding for Academic Documents by Blecher et al. and first released in this repository.

Disclaimer: The team releasing Nougat did not write a model card for this model so this model card has been written by the Hugging Face team.

Note: this model corresponds to the "0.1.0-base" version of the original repository.

Model description

Nougat is a Donut model trained to transcribe scientific PDFs into an easy-to-use markdown format. The model consists of a Swin Transformer as vision encoder, and an mBART model as text decoder.

The model is trained to autoregressively predict the markdown given only the pixels of the PDF image as input.

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Intended uses & limitations

You can use the raw model for transcribing a PDF into Markdown. See the model hub to look for other fine-tuned versions that may interest you.

How to use

We refer to the docs.

BibTeX entry and citation info

@misc{blecher2023nougat,
      title={Nougat: Neural Optical Understanding for Academic Documents},
      author={Lukas Blecher and Guillem Cucurull and Thomas Scialom and Robert Stojnic},
      year={2023},
      eprint={2308.13418},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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