Instructions to use hf-internal-testing/tiny-random-ASTModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ASTModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-ASTModel")# Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-ASTModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ASTModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7999e62882937433300a76570baab5719e92e37ab4b40d1e0768f6f68d2cd3f
- Size of remote file:
- 158 kB
- SHA256:
- ca0807a6a91ed6301617be8a7cc27ec6f1cccccae82a1e33a8355160931a45b6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.