Instructions to use sumet/Test_Trocr_digit_handwriting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumet/Test_Trocr_digit_handwriting with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-text", model="sumet/Test_Trocr_digit_handwriting")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("sumet/Test_Trocr_digit_handwriting") model = AutoModelForMultimodalLM.from_pretrained("sumet/Test_Trocr_digit_handwriting", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from sumet/Test_Trocr_digit_handwriting: direct link, hf CLI and curl.
- Browser
- Download file 2.67 GB
-
https://huggingface.co/sumet/Test_Trocr_digit_handwriting/resolve/main/optimizer.pt
- Command line
-
hf download hf://sumet/Test_Trocr_digit_handwriting/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/sumet/Test_Trocr_digit_handwriting/resolve/main/optimizer.pt
2.67 GB
- Xet hash:
- d7b8fe8d6045bad5efb611df18946ac561267ca493de3b450701425d90573cbc
- Size of remote file:
- 2.67 GB
- SHA256:
- 7616590322292667a9236be8811f1fd731264455df71ce3cee71752f71c90bd0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.