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 pytorch_model.bin 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/pytorch_model.bin
- Command line
-
hf download hf://microsoft/Multilingual-MiniLM-L12-H384/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384/resolve/main/pytorch_model.bin
471 MB
- Xet hash:
- 62e9026ca41b681d6b76345f4aab36fcf4bcd769b262812b7dfefdec4bb7925f
- Size of remote file:
- 471 MB
- SHA256:
- cce170910f4d4f3b45be025508f51ef1ad6a1de69f2d46dce7e4603ad31aaaeb
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