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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bae16b14bc1a7b391b1bcda4d882f89077a54be735edcb0781638310a10a8de2
- Size of remote file:
- 280 kB
- SHA256:
- c4c2f294dac2614c19928cd47261d192c418f560e4f14c2fdbd35cf853d18c96
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.