Instructions to use hf-internal-testing/tiny-random-SplinterModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SplinterModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-SplinterModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SplinterModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SplinterModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2df335e10a87c05e11af33f4eefd9b5f3dd2cdbf029a2ccddb3319775d575192
- Size of remote file:
- 3.93 MB
- SHA256:
- 63e02a19b4a6aacd2a23a4bfacdb7ac47d3bec9143253b7c02111af9f45e9bd2
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