Instructions to use zeromodels/mpnet_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/mpnet_base with ZeroModels:
# pip install -U zeromodels # ZeroModels is pure Keras 3, so pick a backend: "jax", "torch" or "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" from zeromodels import AutoZModel # AutoZModel reads the repo's model_type and loads the matching class. # For a task head use the matching loader, e.g. AutoZMImageClassify / AutoZMDetect / # AutoZMSemanticSegment / AutoZMTextGenerate (see zeromodels.auto). model = AutoZModel.from_weights("zeromodels/mpnet_base") - Keras
How to use zeromodels/mpnet_base with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://zeromodels/mpnet_base") - Notebooks
- Google Colab
- Kaggle
Download model.weights.h5 from zeromodels/mpnet_base: direct link, hf CLI and curl.
- Browser
- Download file 535 MB
-
https://huggingface.co/zeromodels/mpnet_base/resolve/main/model.weights.h5
- Command line
-
hf download hf://zeromodels/mpnet_base/model.weights.h5
-
curl -L -o model.weights.h5 https://huggingface.co/zeromodels/mpnet_base/resolve/main/model.weights.h5
535 MB
- Xet hash:
- 929d7aa4da863b8016cecca8e02955c75fdd457a9d01620da5b78f11e2fd4773
- Size of remote file:
- 535 MB
- SHA256:
- 68ed1bd681ebc732e4239cf9eaefe6793432be417fe84de35557384e289defa5
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