Instructions to use zeromodels/dfine-xlarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/dfine-xlarge 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/dfine-xlarge") - Keras
How to use zeromodels/dfine-xlarge 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/dfine-xlarge") - Notebooks
- Google Colab
- Kaggle
Commit History
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Fix Collection badge link to the current zeromodels collection slug
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Migrate to zeromodels (rename kf_*.json -> zm_*.json, fix refs in config + README, ensure tag + badge)
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Upgrade kf_config.json to v2 (nested config + weight_dtype)
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Upgrade kf_config.json to v2 (nested config + dtype)
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docs: load image processor via from_weights
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docs: Unsloth-style KerasFormers model card for dfine-xlarge
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Upload README.md with huggingface_hub
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Upload kf_preprocessor.json with huggingface_hub
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Upload folder using huggingface_hub
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initial commit
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