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:
- 168726cd73839073e1e335147e7b4da60c804ed088c9da1a62bfd6336cd25210
- Size of remote file:
- 3.95 MB
- SHA256:
- 453445a9d8a211fe312943d9c2bff3f4bd2f6f6ea7d503d41b54395e1f8eae83
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