Instructions to use hf-tiny-model-private/tiny-random-XLNetModel 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-XLNetModel 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-XLNetModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLNetModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-XLNetModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from hf-tiny-model-private/tiny-random-XLNetModel: direct link, hf CLI and curl.
- Browser
- Download file 798 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-XLNetModel/resolve/main/spiece.model
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-XLNetModel/spiece.model
-
curl -L -o spiece.model https://huggingface.co/hf-tiny-model-private/tiny-random-XLNetModel/resolve/main/spiece.model
798 kB
- Xet hash:
- f4be795c85fc2ff4878535e8d1ef251d081812cfe125322de96e8faa7e06be27
- Size of remote file:
- 798 kB
- SHA256:
- 1f8c1c0bc2854d1af911a8550288c1258af5ba50277f3a5c829b98eb86fc5646
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