Instructions to use zeromodels/bart_large_xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/bart_large_xsum 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/bart_large_xsum") - Keras
How to use zeromodels/bart_large_xsum with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://zeromodels/bart_large_xsum") - Notebooks
- Google Colab
- Kaggle
Download model.weights.h5 from zeromodels/bart_large_xsum: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/zeromodels/bart_large_xsum/resolve/main/model.weights.h5
- Command line
-
hf download hf://zeromodels/bart_large_xsum/model.weights.h5
-
curl -L -o model.weights.h5 https://huggingface.co/zeromodels/bart_large_xsum/resolve/main/model.weights.h5
1.63 GB
- Xet hash:
- b571f64fa89b557ce2c095915fec4aaf94e79774097409a95380c8a404575c79
- Size of remote file:
- 1.63 GB
- SHA256:
- d9d7fcc8473550a909be10c7d5f6e0bcaa7a4e9c08dd9220812fe6ddabb0bc16
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