Instructions to use hf-tiny-model-private/tiny-random-LiltModel 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-LiltModel 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-LiltModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LiltModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-LiltModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f1cfac86b7bbce8c0ad88c585e0bde47b97bae4c20090d25c239b33a1c582093
- Size of remote file:
- 295 kB
- SHA256:
- a4ca094f43fc4dfe4b7028f844ac028931ef2542b28a29bd0dfebdfa5b7edeb9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.