Instructions to use zeromodels/bart_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/bart_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/bart_base") - Keras
How to use zeromodels/bart_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/bart_base") - Notebooks
- Google Colab
- Kaggle
Download model.weights.h5 from zeromodels/bart_base: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/zeromodels/bart_base/resolve/main/model.weights.h5
- Command line
-
hf download hf://zeromodels/bart_base/model.weights.h5
-
curl -L -o model.weights.h5 https://huggingface.co/zeromodels/bart_base/resolve/main/model.weights.h5
558 MB
- Xet hash:
- 9a85f494a34aa7847c9301755f9163a3fed79d0089b2eeab4437e4e17d76a4b4
- Size of remote file:
- 558 MB
- SHA256:
- e1e0d593fb453ef1ca3b062584479977e10f4581fa2ad9ade33d76ccb3c2173f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.