File size: 1,165 Bytes
9b15c29 095ee51 9b15c29 eeb5674 9b15c29 9f0c342 9b15c29 cf53d80 9f0c342 9b15c29 9f0c342 e7f38f5 b4f0ddc e7f38f5 b4f0ddc 237104f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 |
---
license: cc-by-4.0
tags:
- nougat
- small
- ocr
---
# nougat-small onnx
https://huggingface.co/facebook/nougat-small but exported to onnx. This is **not quantized**.
```python
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:
```sh
pip uninstall cupy-cuda11x -y
```
## how do da inference?
See [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/b46d3e89e631701ef205297435064ab780c4853a/Nougat/Inference_with_Nougat_to_read_scientific_PDFs.ipynb) or [this basic notebook](https://huggingface.co/pszemraj/nougat-small-onnx/blob/main/nougat-small-onnx-example.ipynb) 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.
|