Instructions to use screenmate/xval_compot_500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use screenmate/xval_compot_500 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="screenmate/xval_compot_500", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("screenmate/xval_compot_500", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 778b0ad3721d34c170e26a55bd86347a999702c5917f5add20d0da5a095667d1
- Size of remote file:
- 7.16 kB
- SHA256:
- a8573e2670cd5e57806ff832d8e3a8b11d231fd34923023bc380d9ddf515a01c
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