Instructions to use LayerFault/format-keras-custom-lambda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use LayerFault/format-keras-custom-lambda with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://LayerFault/format-keras-custom-lambda") - Notebooks
- Google Colab
- Kaggle
File size: 624 Bytes
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"attack_surface": [
"model-format-structure"
],
"control_type": "positive",
"corpus_id": "LF-CH-FMTX-0015",
"difficulty": "adversarial",
"expected_decision": "BLOCK",
"ground_truth": "synthetic-generated",
"layerfault_rule_expectations": {
"candidate_rules": [],
"expected_rules": [
"LF-KERAS-CUSTOM-OBJECT"
],
"must_not_rules": []
},
"oracle_ids": [
"LF-ORACLE-FMTX-0015"
],
"related_cases": [],
"repo_name": "format-keras-custom-lambda",
"safe_fixture": true,
"schema": 2,
"severity": "critical",
"techniques": [
"keras"
],
"transformations": []
}
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