phi-2-prompt-injection-QLoRA

Weights updated at 03/07/2024. (Training epochs increased, accuracy improved than before)

View training code: https://github.com/AIM-Intelligence/phi-2-prompt-injection-QLoRA

Try out the model in: https://aim-intelligence.com

This model is a fine-tuned version of microsoft/phi-2 on the None dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.0000
• eval_precision: 1.0
• eval_recall: 1.0
• eval_f1-score: 1.0
• eval_accuracy: 1.0
• eval_runtime: 16.0258
• eval_samples_per_second: 8.424
• eval_steps_per_second: 1.061
• step: 0

Model description

More information needed

Intended uses & limitations

tokenizer = AutoTokenizer.from_pretrained("ysy970923/phi-2-prompt-injection-QLoRA")
model = AutoModelForSequenceClassification.from_pretrained("ysy970923/phi-2-prompt-injection-QLoRA", load_in_4bit=True, torch_dtype=torch.bfloat16, id2label={0: "SAFE", 1: "INJECTION"})
# LABEL_0 is safe, LABEL_1 is prompt_injection

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 2
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 3.0

Framework versions

• PEFT 0.8.2
• Transformers 4.38.1
• Pytorch 2.2.0+cu121
• Datasets 2.17.1
• Tokenizers 0.15.2