Instructions to use hf-tiny-model-private/tiny-random-SplinterModel 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-SplinterModel 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-SplinterModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-SplinterModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-SplinterModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c310c89c9859024a58ee190613f76b56c1823c746177c6616eb84f3635e9f4b0
- Size of remote file:
- 3.93 MB
- SHA256:
- efb29300e6fc3561692b769fe4a2233cef1999b4473093466609edfe892ca550
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.