Instructions to use Qdrant/multilingual-e5-large-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qdrant/multilingual-e5-large-onnx with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Qdrant/multilingual-e5-large-onnx") model = AutoModel.from_pretrained("Qdrant/multilingual-e5-large-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Qdrant/multilingual-e5-large-onnx: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Qdrant/multilingual-e5-large-onnx/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://Qdrant/multilingual-e5-large-onnx@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Qdrant/multilingual-e5-large-onnx/resolve/refs%2Fpr%2F1/tokenizer.json
17.1 MB
- Xet hash:
- 0abd02c8ae3c446b4b196b4041ef6b8dc3dce7d172091fa5ca5ce17b4eb7d627
- Size of remote file:
- 17.1 MB
- SHA256:
- f59925fcb90c92b894cb93e51bb9b4a6105c5c249fe54ce1c704420ac39b81af
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