Instructions to use jfkback/hypencoder.2_layer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jfkback/hypencoder.2_layer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jfkback/hypencoder.2_layer")# pip install -U transformers accelerate # Load model directly from transformers import HypencoderDualEncoder model = HypencoderDualEncoder.from_pretrained("jfkback/hypencoder.2_layer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jfkback/hypencoder.2_layer: direct link, hf CLI and curl.
- Browser
- Download file 483 MB
-
https://huggingface.co/jfkback/hypencoder.2_layer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jfkback/hypencoder.2_layer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jfkback/hypencoder.2_layer/resolve/main/pytorch_model.bin
483 MB
- Xet hash:
- 907f5b0f348e6e1d82d0f562fb9943e6ad63d3fcd6442beb1a057b61a4ee5b82
- Size of remote file:
- 483 MB
- SHA256:
- 1c0ba0c36beae858fcb594101361f77422b26c52394497619760a96afb771fa8
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