Instructions to use hf-internal-testing/tiny-random-LukeModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LukeModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-LukeModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LukeModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-LukeModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c54a7d55911d91ce495532baafd8803cefc573ae3ead3036a3e5af8c1cfffce
- Size of remote file:
- 6.79 MB
- SHA256:
- a4142e0a40f2b6f155219f09aec8d9e0b4ecac2d48d425285fd70dba57c8c26d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.