README.md

---
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).