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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e30280156378d82e3a943fbfc2cd3b2d1509897a1b01feeb19ad5a91ebad1227
- Size of remote file:
- 6.81 MB
- SHA256:
- bafd529093c44b22733581cec02243debe6a7fde0b82016687867439d8cf24e8
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