Instructions to use tingchih/group_perceiver with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tingchih/group_perceiver with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tingchih/group_perceiver")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tingchih/group_perceiver") model = AutoModel.from_pretrained("tingchih/group_perceiver") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 183485cb0f3d2d8b6d6b3b30e3bd41cfb6cbdd0c27c853495ed3ac2fb027d2e6
- Size of remote file:
- 1.1 GB
- SHA256:
- 3b7d3f8f8110486309bbe149e4778c743bd80c96d2edfafe11c2bb9ee4e6b305
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