Instructions to use hf-internal-testing/tiny-random-LEDModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LEDModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-LEDModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LEDModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-LEDModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 954b73bb62701bc593cb75a99f31bd900c7858b9367858e9e5263b8ac56e07dc
- Size of remote file:
- 1.25 MB
- SHA256:
- da936dde88ae78ef57eacd6aa98fc597abdbfe3bb620a7490bc36140b345ae17
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.