Instructions to use hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering") model = AutoModelForDocumentQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering: direct link, hf CLI and curl.
- Browser
- Download file 459 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-LayoutLMv3ForQuestionAnswering/resolve/main/pytorch_model.bin
459 kB
- Xet hash:
- 13a48c6dafe56157b0e6a6658ad45f311639a50512722fb4a6a62242dc3b9436
- Size of remote file:
- 459 kB
- SHA256:
- 0bdabc8a5324a0b9f957bf9d9c25fab238c6b9c34537dbb1a40e10896f47b233
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.