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:
- 14aead69ed518e9d27aa107e427adfd50ce3b3e6e59dd514210a904cbdef6318
- Size of remote file:
- 558 MB
- SHA256:
- 81008b9d97a060f8841fb0cbbd28372f784793d89f837d846f39ece7e3291fdd
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