model-compression-exploration
Installation
SKILL.md
Model Compression Exploration
Systematically explore weight-only compression configurations for a PyTorch model using coreai_opt. The goal is to present the user with a clear overview of accuracy-vs-size tradeoff options across quantization and palettization, organized into three experiment groups.
Supporting files
| File | Contents |
|---|---|
compression_patterns.md |
Empirical patterns: what works, what doesn't, and why |
size_estimation.md |
How to compute theoretical compressed model size |
experiment_runner.md |
Memory-safe experiment loop, helpers, average bitwidth |
output_report.md |
How to format and organize the output produced |
Bundled scripts
The deterministic helpers are unit-tested and importable. Prefer them over hand-rolled equivalents — they encode formulas and edge cases that have already been debugged.