Instructions to use ShawnyPapa77/cpeg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShawnyPapa77/cpeg with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ShawnyPapa77/cpeg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,688 Bytes
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"description": "Data of Figure 3 (mean ORR against mean APR, input decisions, all units). 'CPEG' and 'Before the patch': per-benchmark counts on the seven harmful benchmarks (apr: [not blocked, units]) and the nine benign benchmarks (orr: [blocked, units]; 'advice' = the advice units of MM-SafetyBench and AIR-Bench pooled); the arrow of the figure goes from 'Before the patch' to 'CPEG'. 'systems' and 'ablations': mean ORR and mean APR as fractions (unrounded means of the per-benchmark rates). The weight-tuned defences of the figure are judged on final answers and are not included.",
"CPEG": {
"apr": {
"harmbench": [
6,
300
],
"xstest": [
0,
200
],
"mmsafetybench": [
24,
1274
],
"airbench": [
46,
1442
],
"wildguardtest": [
105,
754
],
"wildjailbreak": [
108,
2000
],
"taxonomy": [
10,
800
]
},
"orr": {
"xstest": [
3,
250
],
"advice": [
9,
413
],
"wildguardtest": [
50,
945
],
"wildjailbreak": [
24,
210
],
"orbench": [
37,
1319
],
"ethics_cs": [
0,
1704
],
"llm_ethics": [
0,
77
],
"ggb": [
7,
90
],
"morables": [
0,
800
]
}
},
"Before the patch": {
"apr": {
"harmbench": [
7,
300
],
"xstest": [
0,
200
],
"mmsafetybench": [
19,
1274
],
"airbench": [
11,
1442
],
"wildguardtest": [
58,
754
],
"wildjailbreak": [
75,
2000
],
"taxonomy": [
0,
800
]
},
"orr": {
"xstest": [
1,
250
],
"advice": [
8,
413
],
"wildguardtest": [
494,
945
],
"wildjailbreak": [
206,
210
],
"orbench": [
28,
1319
],
"ethics_cs": [
0,
1704
],
"llm_ethics": [
0,
77
],
"ggb": [
0,
90
],
"morables": [
0,
800
]
}
},
"systems": {
"CPEG": {
"mean_orr": 0.034090762970639725,
"mean_apr": 0.03949939109693624
},
"Llama Guard 3 8B": {
"mean_orr": 0.10734475289373784,
"mean_apr": 0.2927797234729178
},
"Shieldstral-1.0-3B": {
"mean_orr": 0.13777680238455245,
"mean_apr": 0.16079966392204798
},
"NeMo Guardrails": {
"mean_orr": 0.283366270150148,
"mean_apr": 0.12530170200168939
},
"WildGuard 7B": {
"mean_orr": 0.2448641017846867,
"mean_apr": 0.10164652419461183
},
"Qwen3Guard-8B strict": {
"mean_orr": 0.20103609385850182,
"mean_apr": 0.09832925483404087
},
"Qwen3Guard-8B loose": {
"mean_orr": 0.07458805124203519,
"mean_apr": 0.1929117917944944
},
"Jev (vendor recipe)": {
"mean_orr": 0.061822348225507966,
"mean_apr": 0.33784212261005236
},
"Jev (τ = 0.5)": {
"mean_orr": 0.1261912520331628,
"mean_apr": 0.13448314740016537
},
"Jev (τ = 0.86)": {
"mean_orr": 0.009647507574259306,
"mean_apr": 0.309336405015289
}
},
"ablations": {
"Energy gate only": {
"mean_orr": 0.02402341441080199,
"mean_apr": 0.04793996068758645
},
"No probes": {
"mean_orr": 0.037825289921979054,
"mean_apr": 0.03280330542139708
},
"8B judge": {
"mean_orr": 0.20263871233253392,
"mean_apr": 0.14113689502959162
},
"Before the patch": {
"mean_orr": 0.17203359632626228,
"mean_apr": 0.022899766009038845
},
"No benchmark items": {
"mean_orr": 0.09552609564841624,
"mean_apr": 0.10980220177520895
}
}
}
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