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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2fd5885f1271047fce4d653972d077acfb3c57c95b4a89a720cbaf330317bf0
- Size of remote file:
- 437 MB
- SHA256:
- ccb720ba4e3e0e6b62afad16237ceb6271b3391b5f43fbe32fdf932c149d7209
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