Instructions to use jfkback/hypencoder.8_layer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jfkback/hypencoder.8_layer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jfkback/hypencoder.8_layer")# Load model directly from transformers import HypencoderDualEncoder model = HypencoderDualEncoder.from_pretrained("jfkback/hypencoder.8_layer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ef11c98ec8421f4df7e8366ba9d8cb69dd28de372bc0006e0feb6adce906c6d
- Size of remote file:
- 596 MB
- SHA256:
- 5e4045f4b7b89053be1518ae045bef1ee64480adbb715d0fb1bd7c3899db53d1
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