Equation 16 · How a Model Actually Gets Small Enough to Run on a Phone
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SparseGPT poses pruning as a per-layer reconstruction problem. For a layer with weight matrix W and a small calibration set of activations X , it looks for a sparse replacement W' that keeps that layer’s output as close as possible to the original:
Sources cited in the article section
- [5] SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot ↗
- [6] A Simple and Effective Pruning Approach for Large Language Models ↗
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