Instructions to use schreon/xnext-lhm_passages_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use schreon/xnext-lhm_passages_encoder with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, DPRContextEncoder tokenizer = AutoTokenizer.from_pretrained("schreon/xnext-lhm_passages_encoder") model = DPRContextEncoder.from_pretrained("schreon/xnext-lhm_passages_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from schreon/xnext-lhm_passages_encoder: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/schreon/xnext-lhm_passages_encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://schreon/xnext-lhm_passages_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/schreon/xnext-lhm_passages_encoder/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- b72c3a5bd5ca91a421808a7c6d5e0bbf2c00d22a3345a21d84a3b98a9e02a4ac
- Size of remote file:
- 437 MB
- SHA256:
- ef748357a5f2f0a559fc8373bf4d5244dc8af1c607fdde8f8189b947addcbdf9
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