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name: twin_uniform_quantization description: Use this skill to apply PTQ4ViT twin uniform quantization to post-softmax or post-GELU activations with range flags.

Twin Uniform Quantization

Use this skill when a recovery or implementation task needs the PTQ4ViT capability described in the module plan. Do not use it to claim full ImageNet accuracy without an actual model, dataset, and evaluation command.

Inputs

• Numeric calibration tensors or an attempt directory, depending on the script.
• Explicit bit width, candidate scales, and runtime/source-boundary constraints.

Outputs

• Deterministic numeric traces suitable for validation.
• JSON-compatible metadata that downstream recovery can inspect.

Workflow

1. Read the module contract and pass only bounded local tensors or attempt artifacts.
2. Run the script in scripts/ or import its pure functions.
3. Validate outputs with the tests in tests/.
4. Record commands and artifacts in the Distiller attempt when used for recovery.

Validation

Run python validate_skill_tree.py <skill_dir> --run-tests from the Distiller module-to-skill validator.

Limitations

This skill preserves the PTQ4ViT mechanism but does not bundle pretrained models, ImageNet, or the original repository. Recovery must not read the original repository.