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This model is a fine-tuned version of mistralai/Mistral-Nemo-Instruct-2407 on the wildjailbreak dataset. Only the adversarial_harmful data types have been used for training.

Uses

This model is intended to be used for red-teaming purposes only. It generates prompts that are likely to evade existing LLMs' content filters based on the user's input.

→ The HarmBench evaluation will be released soon.

Training Hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine_with_restarts
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 1

Results

Epoch Step Validation Loss Training Loss
0.0982 20 1.2933 1.3425
0.1965 40 1.1966 1.2067
0.2947 60 1.1594 1.1544
0.3930 80 1.1386 1.1427
0.4912 100 1.1259 1.1235
0.5895 120 1.1179 1.1167
0.6877 140 1.1129 1.1153
0.7860 160 1.1098 1.1118
0.8842 180 1.1086 1.1112
0.9825 200 1.1083 1.1113
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