Instructions to use hf-tiny-model-private/tiny-random-MarkupLMModel 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-MarkupLMModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-MarkupLMModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-MarkupLMModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-MarkupLMModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fc45b8efab676b3c4af31e776bba81e490e18708e6a9f16c448fdd69bcb4d57b
- Size of remote file:
- 6.94 MB
- SHA256:
- df5ceadec68f83b49b8903122f966ad7f75b203335cf517eb79cb0d4b9c82c23
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.