Instructions to use zeromodels/deberta_v3_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/deberta_v3_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_v3_large") - Keras
How to use zeromodels/deberta_v3_large 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/deberta_v3_large") - Notebooks
- Google Colab
- Kaggle
Download model.weights.h5 from zeromodels/deberta_v3_large: direct link, hf CLI and curl.
- Browser
- Download file 1.74 GB
-
https://huggingface.co/zeromodels/deberta_v3_large/resolve/main/model.weights.h5
- Command line
-
hf download hf://zeromodels/deberta_v3_large/model.weights.h5
-
curl -L -o model.weights.h5 https://huggingface.co/zeromodels/deberta_v3_large/resolve/main/model.weights.h5
1.74 GB
- Xet hash:
- f16c9f2361ec8e8f04b78a1cd5a83ef1e98c2dec426f50b2829dc95207721e49
- Size of remote file:
- 1.74 GB
- SHA256:
- b2a163ab193f0306cb6a5ebde9193764e776a676868aa84d39fa2ace751a94da
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