Instructions to use jamiealexandre/mpnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamiealexandre/mpnet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jamiealexandre/mpnet")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jamiealexandre/mpnet") model = AutoModel.from_pretrained("jamiealexandre/mpnet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jamiealexandre/mpnet: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/jamiealexandre/mpnet/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jamiealexandre/mpnet/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jamiealexandre/mpnet/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- fc3a68ef0891d6cdcd7ecb61bad4b189fdf87c941eb6f4f2a51a5b534e611772
- Size of remote file:
- 1.11 GB
- SHA256:
- 0ed32b840efb639e710aa68e18e197eb2dacd07e95d700125d83f46e566b6c76
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