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:
- 03e395c6fcd1490e7533f95618248e909bfac81b1f6318ebc111ebb36efc4fb9
- Size of remote file:
- 558 MB
- SHA256:
- 03f156727431a8dd892f8560fb27a9b5e975da9e8f10916502d925d3264ba448
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