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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f78eaf8e28f07ea897142fe85359695da0cf0f65ab7e5f13b72ed306be92f6d2
- Size of remote file:
- 596 MB
- SHA256:
- 5dd6758eb9fa90aa188001217e88765e383dad3cf9b037adc218b43998872418
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