metadata
tags:
- mic
- test-artifact
base_model: microsoft/swinv2-tiny-patch4-window16-256
swinv2-tiny-patch4-window16-256-mic
Slim model weights, executable usage and framework expectations in one repository.
- Contract manifest
- Transformers usage
- Transformers expectations
- Fixture provenance
- How the fixture was shrunk
Contract
Swinv2 image classification
Runnable usage and the framework expectations directory are linked above.
Slim model
Slim fixture: swinv2-tiny-patch4-window16-256-mic
A 5.4M-parameter fixture for the Swinv2 MIC, covering microsoft/swinv2-tiny-patch4-window16-256. It is a test artifact, not a usable model, tagged test-artifact to keep it out of candidate-model search.
- Fixture key
73efa506558c. It covers every checkpoint whose processor files are byte-identical and whose config matches after shrinking. - Same architecture, smaller config:
Swinv2ForImageClassification, shrunk by the generic rule insweep.py(num_layers 2). - Processor files copied byte for byte from
microsoft/swinv2-tiny-patch4-window16-256:preprocessor_config.json. - Random weights (seed 17). They passed the input-sensitivity gate: every perturbation of the contract's inputs moved the logits by at least 0.001 (weakest:
pixel_values:reverse, 0.068), so no training was needed. - Same tensor names as the real checkpoint. The real checkpoint has every parameter, and so does the fixture.
- Saved in float32, the dtype
microsoft/swinv2-tiny-patch4-window16-256loads in by default. - Finite in float32, bfloat16, and float16 (forward, loss, gradients).
Built by build_fixtures.py; build.json records the pinned sources, gate results, and file hashes. This repository includes the slim weights, usage and framework expectations; rebuild the other files with python example/build_fixtures.py --fixture swinv2-tiny-patch4-window16-256-mic.