Find files, editable templates and browser test targets by what you need to make or test. The directory below is cut by format; the two collections under it cut the same library by subject and by workflow.
Page 4 of 6; 24 results per page.
The named float32 input tensor payload for the Affine MatMul plus Add model, with explicit shapes and JSON values for runner-independent test setup.
A two-node affine transform with fixed weights and bias, exercising initializers, MatMul dimensions and Add broadcasting. The matching JSON input and expected output make the model immediately usable in a CI inference assertion.
The reference-evaluator output for the Dynamic-Batch ReduceSum model and supplied input, including shape, dtype, exact values and a 1e-6 tolerance.
The named float32 input tensor payload for the Dynamic-Batch ReduceSum model, with explicit shapes and JSON values for runner-independent test setup.
A ReduceSum graph with a symbolic batch dimension and fixed feature width, for dynamic-shape import and batch-output tests. The matching JSON input and expected output make the model immediately usable in a CI inference assertion.
The reference-evaluator output for the Static Identity model and supplied input, including shape, dtype, exact values and a 1e-6 tolerance.
The named float32 input tensor payload for the Static Identity model, with explicit shapes and JSON values for runner-independent test setup.
A one-node identity graph with a fixed 1x4 float tensor, useful as the smallest loader and inference smoke test. The matching JSON input and expected output make the model immediately usable in a CI inference assertion.
The reference-evaluator output for the Two-Input Broadcast Add model and supplied input, including shape, dtype, exact values and a 1e-6 tolerance.
The named float32 input tensor payload for the Two-Input Broadcast Add model, with explicit shapes and JSON values for runner-independent test setup.
A two-input Add graph where a length-two bias broadcasts across a 2x2 matrix, checking input binding and broadcasting rules. The matching JSON input and expected output make the model immediately usable in a CI inference assertion.
OpenAI function tools definition JSON paired with the tool-calling traces.
Pairwise doc preferences for reward-model and reranker training tests.
Parallel multi-tool call payloads for concurrent tool-router tests.
An English↔Spanish parallel corpus in JSON Lines: 20 aligned sentence pairs of everyday phrases. A fixture for training and evaluating machine-translation models and for testing UTF-8 handling of accented characters.
Per-class precision/recall/F1 snapshot for classification dashboards.
PR curve coordinates for average-precision metric tests.
A reusable prompt library in JSON Lines: 20 templated prompts for summarization, translation, extraction, code, and more, each with a task label, tags, and {curly-brace} placeholders. A fixture for prompt-management tools and JSONL parsers.
Tiny SAMPLE prompt fixture (fewshot-translate) for harness and eval wiring.
Tiny SAMPLE prompt fixture (system-concise) for harness and eval wiring.
Tiny SAMPLE prompt fixture (system-json-only) for harness and eval wiring.
Tiny SAMPLE prompt fixture (user-classify) for harness and eval wiring.
Tiny SAMPLE prompt fixture (user-extract-entities) for harness and eval wiring.
Tiny SAMPLE prompt fixture (user-summarize) for harness and eval wiring.
