sal-torch benchmark results
Browse files- README.md +51 -0
- jepa_sal_multiseed_summary.json +295 -0
- multiseed_results.json +707 -0
- robustness_scale_results.json +448 -0
- sal_qat_prototype_seed123.json +423 -0
- sal_qat_prototype_seed42.json +423 -0
- sal_qat_prototype_seed456.json +423 -0
README.md
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---
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license: cc-by-4.0
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tags:
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- pruning
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- compression
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- benchmarks
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- sparsification
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- sal
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size_categories:
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- n<1K
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---
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# sal-torch Benchmarks
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Raw results from sal-torch experiments: SAL-trained vs standard-trained models under compression.
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## Files
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- `multiseed_results.json` — GPT-2 Medium / SST-2, 5 seeds, full 872-example validation split
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- `robustness_scale_results.json` — robustness suite across model scales
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- `jepa_sal_multiseed_summary.json` — I-JEPA ViT-H/14 / ImageNet-100, 3 seeds
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- `sal_qat_prototype_seed{42,123,456}.json` — SAL+QAT on ViT-B/16 / ImageNet-100
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## GPT-2 Medium / SST-2 (5 seeds, mean accuracy)
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| Setting | Standard | SAL | Δ | SAL wins |
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|---|---|---|---|---|
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| dense | 90.05% | 90.62% | +0.57pp | 4/5 |
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| INT8 | 88.88% | 89.61% | +0.73pp | 4/5 |
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| INT4 (NF4) | 90.02% | 90.23% | +0.21pp | 3/5 |
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| prune 33% heads | 85.71% | 88.17% | +2.45pp | 5/5 |
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| prune 50% heads | 80.87% | 82.94% | +2.06pp | 4/5 |
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| prune 33% + INT8 | 82.84% | 84.72% | +1.88pp | 4/5 |
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| prune 33% + INT4 | 85.67% | 87.25% | +1.58pp | 5/5 |
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## Other results
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| Model | Result | Seeds |
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|---|---|---|
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| 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 |
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| ViT-B/16 SAL+QAT | +22pp mean at 50% pruning + INT4 | 3 |
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## Links
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- 📦 [sal-torch on PyPI](https://pypi.org/project/sal-torch/) — `pip install sal-torch`
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- 🔧 [GitHub](https://github.com/zeekmartin/sal-torch)
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- 🌐 [cognitive-engineering.dev](https://cognitive-engineering.dev/sal-torch)
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- 🛡️ [sal-certify](https://cognitive-engineering.dev/sal-certify)
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- 📄 PPAM 2026, Springer LNCS (Paper CP031)
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Data: CC BY 4.0. sal-torch: BSL 1.1. Built by Cognitive Engineering (Switzerland).
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jepa_sal_multiseed_summary.json
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{
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"model": "facebook/ijepa_vith14_1k",
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"dataset": "clane9/imagenet-100",
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"epochs": 5,
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"mask_ratio": 0.3,
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"seeds": [
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42,
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123,
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+
456
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],
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"std": "sample standard deviation (ddof=1) across seeds",
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"win_rule": "SAL wins when sal+X metric > ctrl+X metric (strict); all metrics higher-is-better; CKA vs each arm's own unpruned model",
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"settings": {
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"random-33%": {
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| 15 |
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"probe": {
|
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"sal_mean": 0.7682666579882304,
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| 17 |
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"sal_std": 0.011015156202530312,
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| 18 |
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"ctrl_mean": 0.763866662979126,
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"ctrl_std": 0.013915914108299064,
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| 20 |
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"delta_mean": 0.004399995009104374,
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| 21 |
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"sal_wins": 3,
|
| 22 |
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"n_seeds": 3,
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| 23 |
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"sal_per_seed": {
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| 24 |
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"42": 0.7735999822616577,
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| 25 |
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"123": 0.7555999755859375,
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| 26 |
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"456": 0.775600016117096
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| 27 |
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},
|
| 28 |
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"ctrl_per_seed": {
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| 29 |
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"42": 0.769599974155426,
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| 30 |
+
"123": 0.7480000257492065,
|
| 31 |
+
"456": 0.7739999890327454
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| 32 |
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}
|
| 33 |
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},
|
| 34 |
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"knn": {
|
| 35 |
+
"sal_mean": 0.7116,
|
| 36 |
+
"sal_std": 0.030360500654633478,
|
| 37 |
+
"ctrl_mean": 0.6996,
|
| 38 |
+
"ctrl_std": 0.03555277766926239,
|
| 39 |
+
"delta_mean": 0.01200000000000001,
|
| 40 |
+
"sal_wins": 3,
|
| 41 |
+
"n_seeds": 3,
|
| 42 |
+
"sal_per_seed": {
|
| 43 |
+
"42": 0.736,
|
| 44 |
+
"123": 0.6776,
|
| 45 |
+
"456": 0.7212
|
| 46 |
+
},
|
| 47 |
+
"ctrl_per_seed": {
|
| 48 |
+
"42": 0.7276,
|
| 49 |
+
"123": 0.6596,
|
| 50 |
+
"456": 0.7116
|
| 51 |
+
}
|
| 52 |
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},
|
| 53 |
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"cka": {
|
| 54 |
+
"sal_mean": 0.8455930365795413,
|
| 55 |
+
"sal_std": 0.027458124545189446,
|
| 56 |
+
"ctrl_mean": 0.8383324094042763,
|
| 57 |
+
"ctrl_std": 0.028226907307956534,
|
| 58 |
+
"delta_mean": 0.007260627175264989,
|
| 59 |
+
"sal_wins": 3,
|
| 60 |
+
"n_seeds": 3,
|
| 61 |
+
"sal_per_seed": {
|
| 62 |
+
"42": 0.8362097928669213,
|
| 63 |
+
"123": 0.8240565312321922,
|
| 64 |
+
"456": 0.8765127856395103
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| 65 |
+
},
|
| 66 |
+
"ctrl_per_seed": {
|
| 67 |
+
"42": 0.8301900206988898,
|
| 68 |
+
"123": 0.81507167323972,
|
| 69 |
+
"456": 0.8697355342742193
|
| 70 |
+
}
|
| 71 |
+
}
|
| 72 |
+
},
|
| 73 |
+
"random-50%": {
|
| 74 |
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"probe": {
|
| 75 |
+
"sal_mean": 0.6432000199953715,
|
| 76 |
+
"sal_std": 0.027681046067549394,
|
| 77 |
+
"ctrl_mean": 0.6292000015576681,
|
| 78 |
+
"ctrl_std": 0.024308038593865836,
|
| 79 |
+
"delta_mean": 0.014000018437703377,
|
| 80 |
+
"sal_wins": 3,
|
| 81 |
+
"n_seeds": 3,
|
| 82 |
+
"sal_per_seed": {
|
| 83 |
+
"42": 0.635200023651123,
|
| 84 |
+
"123": 0.6204000115394592,
|
| 85 |
+
"456": 0.6740000247955322
|
| 86 |
+
},
|
| 87 |
+
"ctrl_per_seed": {
|
| 88 |
+
"42": 0.6215999722480774,
|
| 89 |
+
"123": 0.6096000075340271,
|
| 90 |
+
"456": 0.6564000248908997
|
| 91 |
+
}
|
| 92 |
+
},
|
| 93 |
+
"knn": {
|
| 94 |
+
"sal_mean": 0.4748,
|
| 95 |
+
"sal_std": 0.09198869495758706,
|
| 96 |
+
"ctrl_mean": 0.4557333333333333,
|
| 97 |
+
"ctrl_std": 0.07967567592015357,
|
| 98 |
+
"delta_mean": 0.019066666666666676,
|
| 99 |
+
"sal_wins": 3,
|
| 100 |
+
"n_seeds": 3,
|
| 101 |
+
"sal_per_seed": {
|
| 102 |
+
"42": 0.4816,
|
| 103 |
+
"123": 0.3796,
|
| 104 |
+
"456": 0.5632
|
| 105 |
+
},
|
| 106 |
+
"ctrl_per_seed": {
|
| 107 |
+
"42": 0.4708,
|
| 108 |
+
"123": 0.3696,
|
| 109 |
+
"456": 0.5268
|
| 110 |
+
}
|
| 111 |
+
},
|
| 112 |
+
"cka": {
|
| 113 |
+
"sal_mean": 0.6495459152469347,
|
| 114 |
+
"sal_std": 0.08417228908359488,
|
| 115 |
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"ctrl_mean": 0.6392097617626044,
|
| 116 |
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"ctrl_std": 0.08013032868522768,
|
| 117 |
+
"delta_mean": 0.010336153484330368,
|
| 118 |
+
"sal_wins": 3,
|
| 119 |
+
"n_seeds": 3,
|
| 120 |
+
"sal_per_seed": {
|
| 121 |
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"42": 0.6575462812681341,
|
| 122 |
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"123": 0.5616590833748064,
|
| 123 |
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"456": 0.7294323810978636
|
| 124 |
+
},
|
| 125 |
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"ctrl_per_seed": {
|
| 126 |
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"42": 0.654487053986227,
|
| 127 |
+
"123": 0.5525405972927678,
|
| 128 |
+
"456": 0.7106016340088182
|
| 129 |
+
}
|
| 130 |
+
}
|
| 131 |
+
},
|
| 132 |
+
"magnitude-33%": {
|
| 133 |
+
"probe": {
|
| 134 |
+
"sal_mean": 0.7877333362897238,
|
| 135 |
+
"sal_std": 0.0031069674753368207,
|
| 136 |
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"ctrl_mean": 0.7810666561126709,
|
| 137 |
+
"ctrl_std": 0.004105292568559644,
|
| 138 |
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"delta_mean": 0.006666680177052853,
|
| 139 |
+
"sal_wins": 3,
|
| 140 |
+
"n_seeds": 3,
|
| 141 |
+
"sal_per_seed": {
|
| 142 |
+
"42": 0.7851999998092651,
|
| 143 |
+
"123": 0.7868000268936157,
|
| 144 |
+
"456": 0.7911999821662903
|
| 145 |
+
},
|
| 146 |
+
"ctrl_per_seed": {
|
| 147 |
+
"42": 0.7799999713897705,
|
| 148 |
+
"123": 0.7775999903678894,
|
| 149 |
+
"456": 0.7856000065803528
|
| 150 |
+
}
|
| 151 |
+
},
|
| 152 |
+
"knn": {
|
| 153 |
+
"sal_mean": 0.7150666666666666,
|
| 154 |
+
"sal_std": 0.0060706946335105216,
|
| 155 |
+
"ctrl_mean": 0.7066666666666667,
|
| 156 |
+
"ctrl_std": 0.00804321660365638,
|
| 157 |
+
"delta_mean": 0.008399999999999963,
|
| 158 |
+
"sal_wins": 3,
|
| 159 |
+
"n_seeds": 3,
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| 160 |
+
"sal_per_seed": {
|
| 161 |
+
"42": 0.7096,
|
| 162 |
+
"123": 0.714,
|
| 163 |
+
"456": 0.7216
|
| 164 |
+
},
|
| 165 |
+
"ctrl_per_seed": {
|
| 166 |
+
"42": 0.7,
|
| 167 |
+
"123": 0.7044,
|
| 168 |
+
"456": 0.7156
|
| 169 |
+
}
|
| 170 |
+
},
|
| 171 |
+
"cka": {
|
| 172 |
+
"sal_mean": 0.8650120661475882,
|
| 173 |
+
"sal_std": 0.003619987019747775,
|
| 174 |
+
"ctrl_mean": 0.8597783208919433,
|
| 175 |
+
"ctrl_std": 0.0038491480920364667,
|
| 176 |
+
"delta_mean": 0.005233745255644906,
|
| 177 |
+
"sal_wins": 3,
|
| 178 |
+
"n_seeds": 3,
|
| 179 |
+
"sal_per_seed": {
|
| 180 |
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"42": 0.8653772693349372,
|
| 181 |
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"123": 0.861223320353094,
|
| 182 |
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"456": 0.8684356087547332
|
| 183 |
+
},
|
| 184 |
+
"ctrl_per_seed": {
|
| 185 |
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"42": 0.8613785780881761,
|
| 186 |
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"123": 0.8553871863464834,
|
| 187 |
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"456": 0.8625691982411705
|
| 188 |
+
}
|
| 189 |
+
}
|
| 190 |
+
},
|
| 191 |
+
"magnitude-50%": {
|
| 192 |
+
"probe": {
|
| 193 |
+
"sal_mean": 0.6575999855995178,
|
| 194 |
+
"sal_std": 0.006609077488047054,
|
| 195 |
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"ctrl_mean": 0.6483999888102213,
|
| 196 |
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"ctrl_std": 0.00672903143433795,
|
| 197 |
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"delta_mean": 0.009199996789296505,
|
| 198 |
+
"sal_wins": 2,
|
| 199 |
+
"n_seeds": 3,
|
| 200 |
+
"sal_per_seed": {
|
| 201 |
+
"42": 0.6507999897003174,
|
| 202 |
+
"123": 0.6579999923706055,
|
| 203 |
+
"456": 0.6639999747276306
|
| 204 |
+
},
|
| 205 |
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"ctrl_per_seed": {
|
| 206 |
+
"42": 0.6535999774932861,
|
| 207 |
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"123": 0.6407999992370605,
|
| 208 |
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"456": 0.6507999897003174
|
| 209 |
+
}
|
| 210 |
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},
|
| 211 |
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"knn": {
|
| 212 |
+
"sal_mean": 0.4608,
|
| 213 |
+
"sal_std": 0.007662897624267216,
|
| 214 |
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"ctrl_mean": 0.4536,
|
| 215 |
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"ctrl_std": 0.00860697391654003,
|
| 216 |
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"delta_mean": 0.007199999999999984,
|
| 217 |
+
"sal_wins": 3,
|
| 218 |
+
"n_seeds": 3,
|
| 219 |
+
"sal_per_seed": {
|
| 220 |
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"42": 0.4556,
|
| 221 |
+
"123": 0.4572,
|
| 222 |
+
"456": 0.4696
|
| 223 |
+
},
|
| 224 |
+
"ctrl_per_seed": {
|
| 225 |
+
"42": 0.454,
|
| 226 |
+
"123": 0.4448,
|
| 227 |
+
"456": 0.462
|
| 228 |
+
}
|
| 229 |
+
},
|
| 230 |
+
"cka": {
|
| 231 |
+
"sal_mean": 0.6554837695505887,
|
| 232 |
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"sal_std": 0.0058157309930445,
|
| 233 |
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"ctrl_mean": 0.6487758014191404,
|
| 234 |
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"ctrl_std": 0.005838048551784243,
|
| 235 |
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"delta_mean": 0.006707968131448361,
|
| 236 |
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"sal_wins": 3,
|
| 237 |
+
"n_seeds": 3,
|
| 238 |
+
"sal_per_seed": {
|
| 239 |
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"42": 0.6605348853461931,
|
| 240 |
+
"123": 0.6491257974139542,
|
| 241 |
+
"456": 0.6567906258916191
|
| 242 |
+
},
|
| 243 |
+
"ctrl_per_seed": {
|
| 244 |
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"42": 0.652974168139654,
|
| 245 |
+
"123": 0.6421090039603576,
|
| 246 |
+
"456": 0.6512442321574095
|
| 247 |
+
}
|
| 248 |
+
}
|
| 249 |
+
}
|
| 250 |
+
},
|
| 251 |
+
"unpruned": {
|
| 252 |
+
"sal-trained": {
|
| 253 |
+
"probe": {
|
| 254 |
+
"mean": 0.8324000040690104,
|
| 255 |
+
"std": 0.007969940843351848
|
| 256 |
+
},
|
| 257 |
+
"knn": {
|
| 258 |
+
"mean": 0.7974666666666667,
|
| 259 |
+
"std": 0.012613220577367756
|
| 260 |
+
},
|
| 261 |
+
"cka": {
|
| 262 |
+
"mean": 0.9803545765472055,
|
| 263 |
+
"std": 0.0035842681612406166
|
| 264 |
+
}
|
| 265 |
+
},
|
| 266 |
+
"control-trained": {
|
| 267 |
+
"probe": {
|
| 268 |
+
"mean": 0.8314666748046875,
|
| 269 |
+
"std": 0.0038436155499509826
|
| 270 |
+
},
|
| 271 |
+
"knn": {
|
| 272 |
+
"mean": 0.7924,
|
| 273 |
+
"std": 0.0112853887837327
|
| 274 |
+
},
|
| 275 |
+
"cka": {
|
| 276 |
+
"mean": 0.9830107326168132,
|
| 277 |
+
"std": 0.0022673001910359283
|
| 278 |
+
}
|
| 279 |
+
}
|
| 280 |
+
},
|
| 281 |
+
"wins_per_seed": {
|
| 282 |
+
"42": 11,
|
| 283 |
+
"123": 12,
|
| 284 |
+
"456": 12
|
| 285 |
+
},
|
| 286 |
+
"sal_wins_total": 35,
|
| 287 |
+
"comparisons_total": 36,
|
| 288 |
+
"source_files": [
|
| 289 |
+
"data/results/jepa_sal_benchmark.json",
|
| 290 |
+
"data/results/jepa_sal_benchmark_seed123.json",
|
| 291 |
+
"data/results/jepa_sal_benchmark_seed456.json"
|
| 292 |
+
],
|
| 293 |
+
"environment_note": "All seeds on 1x A100-SXM4-80GB, batch 32, 5 epochs, --control. Seed 42: transformers <4.52 (slow image processor), /root/results. Seeds 123/456: torch 2.4.1+cu124, transformers 4.51.3 pinned to match, OMP_NUM_THREADS=12, /workspace/results_seedN. Model weights not transferred.",
|
| 294 |
+
"variance_note": "Cross-seed std is often larger than the SAL-control gap because seed changes the data subset and pruning draw for both arms alike; the paired per-seed comparison (sal_wins) is the relevant test."
|
| 295 |
+
}
|
multiseed_results.json
ADDED
|
@@ -0,0 +1,707 @@
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robustness_scale_results.json
ADDED
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|
| 1 |
+
{
|
| 2 |
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| 3 |
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|
| 4 |
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| 5 |
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| 6 |
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| 7 |
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| 8 |
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| 9 |
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|
| 10 |
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| 11 |
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|
| 12 |
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| 13 |
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| 14 |
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| 15 |
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| 16 |
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| 17 |
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| 18 |
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| 19 |
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| 20 |
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| 21 |
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| 22 |
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|
| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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|
| 28 |
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| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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| 33 |
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| 34 |
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| 35 |
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|
| 36 |
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| 37 |
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| 38 |
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| 39 |
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| 40 |
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| 41 |
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| 42 |
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| 43 |
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| 44 |
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| 45 |
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| 46 |
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 52 |
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| 53 |
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| 54 |
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| 55 |
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| 57 |
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| 58 |
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| 59 |
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| 60 |
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| 61 |
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| 62 |
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| 63 |
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| 64 |
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| 65 |
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| 66 |
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| 67 |
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|
| 68 |
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|
| 69 |
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|
| 70 |
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| 71 |
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| 73 |
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| 75 |
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| 76 |
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| 77 |
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| 78 |
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| 79 |
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|
| 81 |
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|
| 82 |
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| 83 |
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| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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|
| 91 |
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| 92 |
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|
| 93 |
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|
| 94 |
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|
| 95 |
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| 96 |
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|
| 97 |
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|
| 98 |
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| 99 |
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| 100 |
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|
| 101 |
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| 102 |
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| 103 |
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| 104 |
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|
| 105 |
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|
| 106 |
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| 107 |
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|
| 108 |
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| 109 |
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| 110 |
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| 111 |
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| 112 |
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|
| 113 |
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| 114 |
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| 115 |
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| 116 |
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| 117 |
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| 118 |
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| 119 |
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| 120 |
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|
| 121 |
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|
| 122 |
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|
| 123 |
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|
| 124 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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|
| 131 |
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|
| 132 |
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|
| 133 |
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|
| 134 |
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"combined": "1/2"
|
| 135 |
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|
| 136 |
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|
| 137 |
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"quantization": "2/2",
|
| 138 |
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|
| 139 |
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"combined": "2/2"
|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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|
| 144 |
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|
| 145 |
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"baseline/dense",
|
| 146 |
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"baseline/int8",
|
| 147 |
+
"baseline/int4"
|
| 148 |
+
]
|
| 149 |
+
}
|
| 150 |
+
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|
| 151 |
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|
| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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| 159 |
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|
| 160 |
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|
| 161 |
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| 168 |
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| 169 |
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| 176 |
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| 177 |
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| 178 |
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| 179 |
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| 181 |
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| 182 |
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| 183 |
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|
| 184 |
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|
| 185 |
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| 186 |
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| 187 |
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| 191 |
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| 192 |
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| 193 |
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| 194 |
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| 199 |
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| 200 |
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| 201 |
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| 207 |
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| 208 |
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| 209 |
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| 216 |
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|
| 217 |
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| 218 |
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| 219 |
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| 220 |
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| 230 |
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|
| 231 |
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|
| 232 |
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|
| 233 |
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"device": "cuda",
|
| 234 |
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"seconds": 45.7
|
| 235 |
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|
| 236 |
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"int4": {
|
| 237 |
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|
| 238 |
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|
| 239 |
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|
| 240 |
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"backend": "bitsandbytes-nf4",
|
| 241 |
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"device": "cuda",
|
| 242 |
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"seconds": 31.9
|
| 243 |
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|
| 244 |
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|
| 245 |
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|
| 246 |
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|
| 247 |
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|
| 248 |
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|
| 249 |
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|
| 250 |
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|
| 251 |
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|
| 252 |
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|
| 253 |
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|
| 254 |
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|
| 255 |
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|
| 256 |
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|
| 257 |
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|
| 258 |
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|
| 259 |
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|
| 260 |
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|
| 261 |
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|
| 262 |
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|
| 263 |
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|
| 264 |
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|
| 265 |
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|
| 266 |
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|
| 267 |
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|
| 268 |
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|
| 269 |
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|
| 270 |
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|
| 271 |
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|
| 272 |
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|
| 273 |
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"device": "cuda",
|
| 274 |
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|
| 275 |
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|
| 276 |
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|
| 277 |
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|
| 278 |
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"n_eval": 256,
|
| 279 |
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|
| 280 |
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|
| 281 |
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|
| 282 |
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|
| 283 |
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|
| 284 |
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|
| 285 |
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|
| 286 |
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|
| 287 |
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|
| 288 |
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|
| 289 |
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|
| 290 |
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|
| 291 |
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|
| 292 |
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|
| 293 |
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|
| 294 |
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|
| 295 |
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|
| 296 |
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|
| 297 |
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|
| 298 |
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|
| 299 |
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"gpt2_full": {
|
| 300 |
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"tier": "gpt2_full",
|
| 301 |
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|
| 302 |
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|
| 303 |
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|
| 304 |
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|
| 305 |
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|
| 306 |
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|
| 307 |
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|
| 308 |
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|
| 309 |
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|
| 310 |
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|
| 311 |
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|
| 312 |
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|
| 313 |
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| 314 |
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|
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|
| 316 |
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|
| 317 |
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|
| 318 |
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|
| 319 |
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|
| 320 |
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|
| 321 |
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|
| 322 |
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|
| 323 |
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|
| 324 |
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|
| 325 |
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|
| 326 |
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|
| 327 |
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|
| 328 |
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| 329 |
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| 330 |
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|
| 331 |
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|
| 332 |
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|
| 333 |
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|
| 334 |
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|
| 335 |
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|
| 336 |
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|
| 337 |
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| 338 |
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| 339 |
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| 340 |
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| 341 |
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|
| 342 |
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|
| 343 |
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| 344 |
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| 345 |
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| 346 |
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|
| 347 |
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|
| 348 |
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|
| 349 |
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|
| 350 |
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|
| 351 |
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| 352 |
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| 353 |
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| 354 |
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| 355 |
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| 356 |
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| 357 |
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| 358 |
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|
| 359 |
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| 360 |
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| 361 |
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| 362 |
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| 363 |
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| 364 |
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| 365 |
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| 366 |
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|
| 367 |
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|
| 368 |
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|
| 369 |
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| 370 |
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| 371 |
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| 372 |
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|
| 373 |
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| 374 |
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| 375 |
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| 376 |
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|
| 377 |
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| 378 |
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| 379 |
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| 382 |
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|
| 383 |
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|
| 384 |
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|
| 385 |
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| 386 |
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| 387 |
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| 388 |
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|
| 389 |
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| 390 |
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| 391 |
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| 392 |
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|
| 393 |
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|
| 394 |
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|
| 395 |
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| 396 |
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|
| 397 |
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|
| 398 |
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|
| 399 |
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"device": "cuda",
|
| 400 |
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"seconds": 13.5
|
| 401 |
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|
| 402 |
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|
| 403 |
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|
| 404 |
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|
| 405 |
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|
| 406 |
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|
| 407 |
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|
| 408 |
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"seconds": 12.6
|
| 409 |
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|
| 410 |
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"prune33+int8": {
|
| 411 |
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|
| 412 |
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|
| 413 |
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|
| 414 |
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|
| 415 |
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"device": "cuda",
|
| 416 |
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"seconds": 18.3
|
| 417 |
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|
| 418 |
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"prune33+int4": {
|
| 419 |
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|
| 420 |
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|
| 421 |
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|
| 422 |
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"backend": "bitsandbytes-nf4",
|
| 423 |
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"device": "cuda",
|
| 424 |
+
"seconds": 22.8
|
| 425 |
+
}
|
| 426 |
+
},
|
| 427 |
+
"verdict": {
|
| 428 |
+
"n_eval": 512,
|
| 429 |
+
"noise_floor": 0.001953125,
|
| 430 |
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"absolute": {
|
| 431 |
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|
| 432 |
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"pruning": "2/2",
|
| 433 |
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"combined": "2/2"
|
| 434 |
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|
| 435 |
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"retention": {
|
| 436 |
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"quantization": "2/2",
|
| 437 |
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"pruning": "2/2",
|
| 438 |
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"combined": "2/2"
|
| 439 |
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},
|
| 440 |
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|
| 441 |
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"dense_sal": 0.888671875,
|
| 442 |
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|
| 443 |
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"pareto_front": [
|
| 444 |
+
"SAL/int4"
|
| 445 |
+
]
|
| 446 |
+
}
|
| 447 |
+
}
|
| 448 |
+
}
|
sal_qat_prototype_seed123.json
ADDED
|
@@ -0,0 +1,423 @@
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| 1 |
+
{
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| 2 |
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"model": "google/vit-base-patch16-224",
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| 3 |
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"dataset": "clane9/imagenet-100",
|
| 4 |
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"smoke": false,
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| 5 |
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"seed": 123,
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| 6 |
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"epochs": 5,
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| 7 |
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| 8 |
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"n_train": 20000,
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| 9 |
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"n_val": 5000,
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| 10 |
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"num_classes": 100,
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| 11 |
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"mask_ratio": 0.3,
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| 12 |
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"qat_bits": 4,
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| 13 |
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"prune_seeds": [
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| 14 |
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123,
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| 15 |
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124,
|
| 16 |
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125
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| 17 |
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],
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| 18 |
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"device": "cuda",
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| 19 |
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"vram_gb": 85.1,
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| 20 |
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"lr": {
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| 21 |
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"backbone": 5e-05,
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| 22 |
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"head": 0.001,
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| 23 |
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"weight_decay": 0.05,
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| 24 |
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"warmup_fraction": 0.05,
|
| 25 |
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"schedule": "linear warmup + cosine"
|
| 26 |
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},
|
| 27 |
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"caveats": {
|
| 28 |
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"quantization": "int8/int4 accuracy = per-output-channel symmetric weight round-trip on every nn.Linear except the classifier; the same grid QAT trains against. *_nf4 = real bitsandbytes NF4.",
|
| 29 |
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"size": "computed: fp16 non-quantized params, bits/8 bytes per quantized weight + one fp16 scale per output channel",
|
| 30 |
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"latency": "measured once per setting (architecture-dependent only). int8 CPU latency is torch.ao dynamic INT8; no CPU INT4 kernel exists in stock PyTorch, so int4 latency is null.",
|
| 31 |
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"pruning": "uniform random heads per layer, sliced with slice_heads(); mean over prune_seeds; identical selections for every variant",
|
| 32 |
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"fi": "fi_score on the dense trained model only",
|
| 33 |
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"seed": "one training seed; treat sub-1pp gaps as not shown"
|
| 34 |
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},
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| 35 |
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| 36 |
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| 37 |
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"latency_cpu_ms": 25.331108830869198,
|
| 415 |
+
"latency_gpu_ms": null
|
| 416 |
+
}
|
| 417 |
+
},
|
| 418 |
+
"figures": [
|
| 419 |
+
"accuracy_by_compression.png",
|
| 420 |
+
"size_vs_accuracy.png"
|
| 421 |
+
],
|
| 422 |
+
"wall_clock_s": 726.0
|
| 423 |
+
}
|
sal_qat_prototype_seed42.json
ADDED
|
@@ -0,0 +1,423 @@
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|
| 1 |
+
{
|
| 2 |
+
"model": "google/vit-base-patch16-224",
|
| 3 |
+
"dataset": "clane9/imagenet-100",
|
| 4 |
+
"smoke": false,
|
| 5 |
+
"seed": 42,
|
| 6 |
+
"epochs": 5,
|
| 7 |
+
"batch_size": 64,
|
| 8 |
+
"n_train": 20000,
|
| 9 |
+
"n_val": 5000,
|
| 10 |
+
"num_classes": 100,
|
| 11 |
+
"mask_ratio": 0.3,
|
| 12 |
+
"qat_bits": 4,
|
| 13 |
+
"prune_seeds": [
|
| 14 |
+
42,
|
| 15 |
+
43,
|
| 16 |
+
44
|
| 17 |
+
],
|
| 18 |
+
"device": "cuda",
|
| 19 |
+
"vram_gb": 85.1,
|
| 20 |
+
"lr": {
|
| 21 |
+
"backbone": 5e-05,
|
| 22 |
+
"head": 0.001,
|
| 23 |
+
"weight_decay": 0.05,
|
| 24 |
+
"warmup_fraction": 0.05,
|
| 25 |
+
"schedule": "linear warmup + cosine"
|
| 26 |
+
},
|
| 27 |
+
"caveats": {
|
| 28 |
+
"quantization": "int8/int4 accuracy = per-output-channel symmetric weight round-trip on every nn.Linear except the classifier; the same grid QAT trains against. *_nf4 = real bitsandbytes NF4.",
|
| 29 |
+
"size": "computed: fp16 non-quantized params, bits/8 bytes per quantized weight + one fp16 scale per output channel",
|
| 30 |
+
"latency": "measured once per setting (architecture-dependent only). int8 CPU latency is torch.ao dynamic INT8; no CPU INT4 kernel exists in stock PyTorch, so int4 latency is null.",
|
| 31 |
+
"pruning": "uniform random heads per layer, sliced with slice_heads(); mean over prune_seeds; identical selections for every variant",
|
| 32 |
+
"fi": "fi_score on the dense trained model only",
|
| 33 |
+
"seed": "one training seed; treat sub-1pp gaps as not shown"
|
| 34 |
+
},
|
| 35 |
+
"training": {
|
| 36 |
+
"baseline": {
|
| 37 |
+
"losses": [
|
| 38 |
+
0.7837282361897131,
|
| 39 |
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0.0754672627291306,
|
| 40 |
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0.015972718612451593,
|
| 41 |
+
0.003821871717633031,
|
| 42 |
+
0.0018737513191489199
|
| 43 |
+
],
|
| 44 |
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"masker_stats": null,
|
| 45 |
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"qat_backend": null,
|
| 46 |
+
"train_seconds": 120.6,
|
| 47 |
+
"steps": 1565
|
| 48 |
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},
|
| 49 |
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"sal": {
|
| 50 |
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"losses": [
|
| 51 |
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|
| 52 |
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|
| 53 |
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|
| 54 |
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|
| 55 |
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|
| 56 |
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],
|
| 57 |
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"masker_stats": {
|
| 58 |
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"active": true,
|
| 59 |
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"total_heads": 144,
|
| 60 |
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"active_heads": 101,
|
| 61 |
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"pruned_heads": 43,
|
| 62 |
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"prune_events": 43,
|
| 63 |
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"prune_fraction": 0.3
|
| 64 |
+
},
|
| 65 |
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"qat_backend": null,
|
| 66 |
+
"train_seconds": 121.4,
|
| 67 |
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"steps": 1565
|
| 68 |
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},
|
| 69 |
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"qat": {
|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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"qat_backend": "torch.ao",
|
| 79 |
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"train_seconds": 157.9,
|
| 80 |
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"steps": 1565
|
| 81 |
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},
|
| 82 |
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"sal_qat": {
|
| 83 |
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"losses": [
|
| 84 |
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|
| 85 |
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| 86 |
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| 87 |
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| 89 |
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],
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| 90 |
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|
| 91 |
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"active": true,
|
| 92 |
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"total_heads": 144,
|
| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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"prune_fraction": 0.3
|
| 97 |
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},
|
| 98 |
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"qat_backend": "torch.ao",
|
| 99 |
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"train_seconds": 164.2,
|
| 100 |
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"steps": 1565
|
| 101 |
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}
|
| 102 |
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},
|
| 103 |
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"results": {
|
| 104 |
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"baseline": {
|
| 105 |
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|
| 106 |
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"accuracy": 0.9386,
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| 107 |
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| 108 |
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| 110 |
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| 111 |
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| 112 |
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},
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| 113 |
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| 114 |
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| 115 |
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| 118 |
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| 119 |
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|
| 120 |
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],
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| 121 |
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| 122 |
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| 123 |
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| 130 |
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|
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|
| 132 |
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| 133 |
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|
| 134 |
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| 135 |
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| 136 |
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|
| 138 |
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| 139 |
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| 140 |
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| 141 |
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| 142 |
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| 143 |
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| 145 |
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|
| 154 |
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|
| 155 |
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| 157 |
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|
| 161 |
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|
| 162 |
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|
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|
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|
| 170 |
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|
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|
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|
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|
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|
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|
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|
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|
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| 209 |
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| 210 |
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| 211 |
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| 212 |
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| 213 |
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| 214 |
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| 215 |
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| 216 |
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| 217 |
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| 218 |
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| 226 |
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| 227 |
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| 228 |
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| 229 |
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| 230 |
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| 231 |
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| 232 |
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| 233 |
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| 234 |
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| 235 |
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| 236 |
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| 237 |
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| 238 |
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| 239 |
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| 242 |
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| 243 |
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| 244 |
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| 245 |
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| 250 |
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| 252 |
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| 273 |
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| 274 |
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| 296 |
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| 297 |
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| 298 |
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| 312 |
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| 314 |
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| 319 |
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| 321 |
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| 322 |
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| 329 |
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| 331 |
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| 339 |
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| 343 |
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| 370 |
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| 378 |
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|
| 379 |
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|
| 380 |
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|
| 381 |
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|
| 382 |
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| 383 |
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|
| 384 |
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|
| 385 |
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|
| 386 |
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| 387 |
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|
| 388 |
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| 389 |
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|
| 390 |
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|
| 391 |
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|
| 392 |
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|
| 393 |
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|
| 395 |
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|
| 396 |
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| 397 |
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| 398 |
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|
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| 401 |
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| 402 |
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|
| 403 |
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|
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|
| 405 |
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|
| 406 |
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|
| 407 |
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|
| 408 |
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|
| 409 |
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|
| 410 |
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|
| 411 |
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|
| 412 |
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|
| 413 |
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|
| 414 |
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|
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|
| 416 |
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|
| 417 |
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},
|
| 418 |
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"figures": [
|
| 419 |
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"accuracy_by_compression.png",
|
| 420 |
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"size_vs_accuracy.png"
|
| 421 |
+
],
|
| 422 |
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"wall_clock_s": 723.0
|
| 423 |
+
}
|
sal_qat_prototype_seed456.json
ADDED
|
@@ -0,0 +1,423 @@
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|
| 1 |
+
{
|
| 2 |
+
"model": "google/vit-base-patch16-224",
|
| 3 |
+
"dataset": "clane9/imagenet-100",
|
| 4 |
+
"smoke": false,
|
| 5 |
+
"seed": 456,
|
| 6 |
+
"epochs": 5,
|
| 7 |
+
"batch_size": 64,
|
| 8 |
+
"n_train": 20000,
|
| 9 |
+
"n_val": 5000,
|
| 10 |
+
"num_classes": 100,
|
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456,
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457,
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| 16 |
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458
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| 26 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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