Instructions to use EnigmaConsultant/huntr-poc-modelscan-keras-vectorizedmap-lambda-carrier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use EnigmaConsultant/huntr-poc-modelscan-keras-vectorizedmap-lambda-carrier with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://EnigmaConsultant/huntr-poc-modelscan-keras-vectorizedmap-lambda-carrier") - Notebooks
- Google Colab
- Kaggle
File size: 421 Bytes
f8ecd67 | 1 | {"summary": {"total_issues_by_severity": {"LOW": 0, "MEDIUM": 0, "HIGH": 0, "CRITICAL": 0}, "total_issues": 0, "input_path": "evil_vectorizedmap.keras", "absolute_path": "/home/kali/hunt-workspace/cntk-v2-asmatrix-divzero-sigfpe/vmap_poc", "modelscan_version": "0.8.8", "timestamp": "2026-07-19T05:43:08.867437", "scanned": {"total_scanned": 1, "scanned_files": ["evil_vectorizedmap.keras"]}}, "issues": [], "errors": []} |