Instructions to use KuanP/test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KuanP/test_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="KuanP/test_model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("KuanP/test_model") model = AutoModel.from_pretrained("KuanP/test_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f60dc9ea335dc5e93a48b09c969576fad14765f4b7bfe17af29adb003c177f2
- Size of remote file:
- 438 MB
- SHA256:
- 8162efde1aaf0f20e8a21f8a43e9d47a3f4032cf2eaf9a8ac6ebe87b8246c8a3
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