Instructions to use hurtmongoose/phi2-lora-malicious-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hurtmongoose/phi2-lora-malicious-classifier with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("microsoft/phi-2") model = PeftModel.from_pretrained(base_model, "hurtmongoose/phi2-lora-malicious-classifier") - Transformers
How to use hurtmongoose/phi2-lora-malicious-classifier with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hurtmongoose/phi2-lora-malicious-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,046 Bytes
98c6413 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | {
"eval_loss": 0.462687611579895,
"eval_accuracy": 0.8475894245723172,
"eval_precision_weighted": 0.8428169632185296,
"eval_recall_weighted": 0.8475894245723172,
"eval_f1_weighted": 0.8440311242475405,
"eval_MCC": 0.751470722356998,
"eval_balanced_accuracy": 0.7994490993426729,
"eval_per_class": {
"jailbreaking": {
"TP": 259,
"FP": 97,
"FN": 130,
"TN": 1443,
"FNR": 0.3341902313624679,
"FPR": 0.06298701298701298,
"Specificity": 0.937012987012987
},
"prompt injection": {
"TP": 434,
"FP": 97,
"FN": 136,
"TN": 1262,
"FNR": 0.23859649122807017,
"FPR": 0.07137601177336277,
"Specificity": 0.9286239882266373
},
"unharmful": {
"TP": 942,
"FP": 100,
"FN": 28,
"TN": 859,
"FNR": 0.0288659793814433,
"FPR": 0.10427528675703858,
"Specificity": 0.8957247132429614
}
},
"eval_runtime": 68.6103,
"eval_samples_per_second": 28.115,
"eval_steps_per_second": 3.527,
"epoch": 10.0
} |