Instructions to use microsoft/Multilingual-MiniLM-L12-H384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/Multilingual-MiniLM-L12-H384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="microsoft/Multilingual-MiniLM-L12-H384")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/Multilingual-MiniLM-L12-H384", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from microsoft/Multilingual-MiniLM-L12-H384: direct link, hf CLI and curl.
- Browser
- Download file 471 MB
-
https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://microsoft/Multilingual-MiniLM-L12-H384/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384/resolve/main/flax_model.msgpack
471 MB
- Xet hash:
- f9598f283c600826bfc07c4a85be7398a86785dad32130dab552f1047a3199c0
- Size of remote file:
- 471 MB
- SHA256:
- e5293741fa476159c33cfd82bdbb89229e096c6980790524de3a0e45ba222e09
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