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nougat-small-onnx
pszemraj/nougat-small-onnx
nougat-small-onnx is a image-text-to-text model from pszemraj. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as cc-by-4.0.
https://huggingface.co/facebook/nougat-small but exported to onnx. This is not quantized.
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At a glance
- Task
- Image-Text-to-Text
- Library
- transformers
- License
- cc-by-4.0
- Model type
- vision-encoder-decoder
- Access
- Public
- Created
- Nov 22, 2023
- Updated
- Dec 29, 2025
- SHA
- debafa7e
Try a prompt
Model card
From the Hugging Face model README
nougat-small onnx
https://huggingface.co/facebook/nougat-small but exported to onnx. This is not quantized.
from transformers import NougatProcessor
from optimum.onnxruntime import ORTModelForVision2Seq
model_name = 'pszemraj/nougat-small-onnx'
processor = NougatProcessor.from_pretrained(model_name)
model = ORTModelForVision2Seq.from_pretrained(
model_name,
provider="CPUExecutionProvider", # 'CUDAExecutionProvider' for gpu
use_merged=False,
use_io_binding=True
)
on colab CPU-only (at time of writing) you may get CuPy errors, to solve this uninstall it:
pip uninstall cupy-cuda11x -y
how do da inference?
See here or this basic notebook I uploaded. It seems ONNX brings CPU inference times to 'feasible' - it took ~15 mins for Attention is All You Meme on Colab free CPU runtime.
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- License
- cc-by-4.0
- Created
- Nov 22, 2023
- Updated
- Dec 29, 2025