Small, validated ONNX graphs paired with named JSON inputs and expected tensor outputs. Use them to test runtime loading, dtype and shape handling, broadcasting, dynamic batches, reduction behavior, and numeric tolerances without downloading a production-sized model.
Each card starts with one representative test file. Choose a use case, search, or filter to find a specific profile; a card’s variant menu contains its related samples.
4 test families · 12 available files · Page 1 of 1
Model Inference
The named float32 input tensor payload for the Affine MatMul plus Add model, with explicit shapes and JSON values for runner-independent test setup.
Model Inference
The named float32 input tensor payload for the Dynamic-Batch ReduceSum model, with explicit shapes and JSON values for runner-independent test setup.
Model Inference
The named float32 input tensor payload for the Static Identity model, with explicit shapes and JSON values for runner-independent test setup.
Model Inference
The reference-evaluator output for the Two-Input Broadcast Add model and supplied input, including shape, dtype, exact values and a 1e-6 tolerance.
