Instructions to use LLMTestSaurav/donut-all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLMTestSaurav/donut-all with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LLMTestSaurav/donut-all") model = AutoModel.from_pretrained("LLMTestSaurav/donut-all", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from LLMTestSaurav/donut-all: direct link, hf CLI and curl.
- Browser
- Download file 553 Bytes
-
https://huggingface.co/LLMTestSaurav/donut-all/resolve/main/processor_config.json
- Command line
-
hf download hf://LLMTestSaurav/donut-all/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/LLMTestSaurav/donut-all/resolve/main/processor_config.json
553 Bytes
| { | |
| "image_processor": { | |
| "data_format": "channels_first", | |
| "do_align_long_axis": false, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "do_thumbnail": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "DonutImageProcessorFast", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 720, | |
| "width": 960 | |
| } | |
| }, | |
| "processor_class": "DonutProcessor" | |
| } | |