Instructions to use fsabiu/keras-modelscan-torchmodulewrapper-coverage-gap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use fsabiu/keras-modelscan-torchmodulewrapper-coverage-gap with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://fsabiu/keras-modelscan-torchmodulewrapper-coverage-gap") - Notebooks
- Google Colab
- Kaggle
EXP-MFV-20260502-002 Summary
| File | Detector findings | ModelScan issues | Gap |
|---|---|---|---|
benign_dense.keras |
0 |
0 |
no |
benign_external_vocab_lookup.keras |
0 |
0 |
no |
benign_functional.keras |
0 |
0 |
no |
benign_lambda.keras |
2 |
1 |
no |
benign_registered_custom.keras |
0 |
0 |
no |
benign_torch_module.keras |
1 |
0 |
yes |
Detector Findings
benign_lambda.keras
unsafe_classat$.config.layers[1]: Lambda layer may deserialize Python functions/lambdas.serialized_lambdaat$.config.layers[1].config.function: Serialized Python lambda object.
benign_torch_module.keras
unsafe_classat$.config.layers[1]: TorchModuleWrapper may deserialize torch.nn.Module via torch.load().