| --- |
| license: cc-by-4.0 |
| tags: |
| - pruning |
| - compression |
| - benchmarks |
| - sparsification |
| - sal |
| size_categories: |
| - n<1K |
| --- |
| |
| # sal-torch Benchmarks |
|
|
| Raw results from sal-torch experiments: SAL-trained vs standard-trained models under compression. |
|
|
| ## Files |
|
|
| - `multiseed_results.json` — GPT-2 Medium / SST-2, 5 seeds, full 872-example validation split |
| - `robustness_scale_results.json` — robustness suite across model scales |
| - `jepa_sal_multiseed_summary.json` — I-JEPA ViT-H/14 / ImageNet-100, 3 seeds |
| - `sal_qat_prototype_seed{42,123,456}.json` — SAL+QAT on ViT-B/16 / ImageNet-100 |
|
|
| ## GPT-2 Medium / SST-2 (5 seeds, mean accuracy) |
|
|
| | Setting | Standard | SAL | Δ | SAL wins | |
| |---|---|---|---|---| |
| | dense | 90.05% | 90.62% | +0.57pp | 4/5 | |
| | INT8 | 88.88% | 89.61% | +0.73pp | 4/5 | |
| | INT4 (NF4) | 90.02% | 90.23% | +0.21pp | 3/5 | |
| | prune 33% heads | 85.71% | 88.17% | +2.45pp | 5/5 | |
| | prune 50% heads | 80.87% | 82.94% | +2.06pp | 4/5 | |
| | prune 33% + INT8 | 82.84% | 84.72% | +1.88pp | 4/5 | |
| | prune 33% + INT4 | 85.67% | 87.25% | +1.58pp | 5/5 | |
|
|
| ## Other results |
|
|
| | Model | Result | Seeds | |
| |---|---|---| |
| | I-JEPA ViT-H/14 (631M) | SAL wins 35/36 comparisons at 33/50% head pruning (+0.4 to +1.9pp); no effect at 70% | 3 | |
| | ViT-B/16 SAL+QAT | +22pp mean at 50% pruning + INT4 | 3 | |
|
|
| ## Links |
|
|
| - 📦 [sal-torch on PyPI](https://pypi.org/project/sal-torch/) — `pip install sal-torch` |
| - 🔧 [GitHub](https://github.com/zeekmartin/sal-torch) |
| - 🌐 [cognitive-engineering.dev](https://cognitive-engineering.dev/sal-torch) |
| - 🛡️ [sal-certify](https://cognitive-engineering.dev/sal-certify) |
| - 📄 PPAM 2026, Springer LNCS (Paper CP031) |
|
|
| Data: CC BY 4.0. sal-torch: BSL 1.1. Built by Cognitive Engineering (Switzerland). |
|
|
