Instructions to use zeromodels/deberta_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/deberta_large 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/deberta_large") - Keras
How to use zeromodels/deberta_large 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/deberta_large") - Notebooks
- Google Colab
- Kaggle
Download model.weights.h5 from zeromodels/deberta_large: direct link, hf CLI and curl.
- Browser
- Download file 1.83 GB
-
https://huggingface.co/zeromodels/deberta_large/resolve/main/model.weights.h5
- Command line
-
hf download hf://zeromodels/deberta_large/model.weights.h5
-
curl -L -o model.weights.h5 https://huggingface.co/zeromodels/deberta_large/resolve/main/model.weights.h5
1.83 GB
- Xet hash:
- abbac688009fd95d39c5204d7f088fa215d411a1d8e6612dbebb15bf4f65a8e4
- Size of remote file:
- 1.83 GB
- SHA256:
- 82209849c4e2e291252606813c0e3c42047027a2630415c4c7d6a31bccd41665
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