Instructions to use schreon/xnext-lhm_queries_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use schreon/xnext-lhm_queries_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="schreon/xnext-lhm_queries_encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("schreon/xnext-lhm_queries_encoder") model = AutoModel.from_pretrained("schreon/xnext-lhm_queries_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from schreon/xnext-lhm_queries_encoder: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/schreon/xnext-lhm_queries_encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://schreon/xnext-lhm_queries_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/schreon/xnext-lhm_queries_encoder/resolve/main/pytorch_model.bin
437 MB
- Xet hash:
- e2fd5885f1271047fce4d653972d077acfb3c57c95b4a89a720cbaf330317bf0
- Size of remote file:
- 437 MB
- SHA256:
- ccb720ba4e3e0e6b62afad16237ceb6271b3391b5f43fbe32fdf932c149d7209
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