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Compatibility Matrix (Formats vs Optional Dependencies)

This page shows which model formats work in base install and which require optional dependencies.

Installation profiles

  • Broad portable coverage: pip install "modelaudit[all]" (ONNX installs on Python 3.10-3.12; on Python 3.11-3.12, add tensorflow only for TensorFlow-dependent checkpoint/weight analysis)
  • Minimal base install: pip install modelaudit
  • Targeted extras: install only the extras you need (examples below)

Matrix

Format family Common extensions Base install Optional dependency / extra
Pickle family .pkl, .pickle, .dill Yes modelaudit[dill] for broader dill compatibility
PyTorch archive/binary .pt, .pth, .ckpt, .bin Yes (static archive/pickle checks) modelaudit[pytorch] optional for broader Torch ecosystem tooling
NumPy .npy, .npz Yes None
R serialized .rds, .rda, .rdata Yes (static analysis only) None
TensorFlow SavedModel/MetaGraph .pb, .meta, SavedModel directories Yes (vendored protos) modelaudit[tensorflow] on Python 3.11-3.12 for TensorFlow-dependent checkpoint/weight analysis
Keras H5 .h5, .hdf5 No modelaudit[h5] (required)
ONNX .onnx No modelaudit[onnx] on Python 3.10-3.12 (required)
CoreML .mlmodel Yes (static protobuf/metadata checks) None
NeMo .nemo Yes (static tar/config analysis, Hydra _target_ checks) None
CNTK native .dnn, .cmf Yes (static signature and string analysis) None
RKNN models .rknn Yes (static bounded metadata checks) None
Torch7 serialized .t7, .th, .net Yes (static string/structure checks) None
CatBoost native .cbm Yes (static bounded metadata inspection) None
LightGBM native .lgb, .lightgbm, signature-validated .model Yes (static native-text/binary checks) None
Llamafile binaries .llamafile, extensionless, .exe Yes (executable + embedded GGUF checks) None required
TorchServe archives .mar Yes None
SafeTensors .safetensors Yes None required
GGUF/GGML .gguf, .ggml, .ggmf, .ggjt, .ggla, .ggsa Yes None required
Flax/JAX msgpack .msgpack, .flax, .orbax, .jax Yes None (modelaudit[flax] is a compatibility alias)
JAX checkpoints .ckpt, .checkpoint, .orbax-checkpoint Yes None
TFLite .tflite No modelaudit[tflite] (required)
XGBoost .bst, .model, .json, .ubj Yes for static checks on common formats modelaudit[xgboost] recommended for UBJ/full validation paths
TensorRT .engine, .plan, .trt Yes None required
PaddlePaddle .pdmodel, .pdiparams Yes (static byte-pattern checks) None required
MXNet *-symbol.json, *-NNNN.params Yes (static graph + params checks) None required
Standalone compressed wrappers .gz, .bz2, .xz, .lz4, .zlib Yes (safe bounded decompression + inner scan routing) lz4 package optional only for .lz4 payload decompression
7-Zip archives .7z No modelaudit[sevenzip] (required)
RAR archives .rar Yes (recognized and failed closed as unsupported) None
Archives/config/text .zip, .tar*, .json, .yaml, .yml, .toml, .md, .txt Yes None

Notes

  • Scanner selection is extension- and content-aware; overlapping extensions may be dispatched to different scanners based on file content.
  • Runtime scanner selection is available with modelaudit scan --scanners ... and --exclude-scanner ...; use modelaudit scan --list-scanners to discover scanner IDs.
  • Compressed wrappers enforce limits via compressed_max_decompressed_bytes, compressed_max_decompression_ratio, and compressed_max_depth.
  • R serialized (.rds/.rda/.rdata) support is static-only: ModelAudit does not execute R code or evaluate objects in an R runtime.
  • CNTK scanner scope in v1 is .dnn/.cmf; .model remains owned by XGBoost overlap handling.
  • Llamafile wrappers are executable by design: executable presence is reported at INFO, and severity escalates only when suspicious runtime indicators or malformed embedded payloads are found.
  • RAR archives are recognized so they do not disappear from directory scans; ModelAudit reports them as unsupported coverage with a non-clean result.
  • modelaudit doctor --show-failed shows unavailable scanners and missing dependencies in your environment.
  • If you need predictable CI behavior across many formats, prefer modelaudit[all]; ONNX is included on Python 3.10-3.12, and TensorFlow runtime-dependent paths require adding modelaudit[tensorflow] on Python 3.11-3.12.