Instructions to use jfkback/hypencoder.6_layer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jfkback/hypencoder.6_layer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jfkback/hypencoder.6_layer")# Load model directly from transformers import HypencoderDualEncoder model = HypencoderDualEncoder.from_pretrained("jfkback/hypencoder.6_layer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15cff58720c5bd2e132d7d2c559a0072d4368e4f1b3d28014d29276d34048585
- Size of remote file:
- 558 MB
- SHA256:
- d0cc9fcf501954c9efc8073ebf5dd50cafce9df0ebf6e3a5a8ebdeda01dc5d36
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