attn_implementation: sdpa
backdoor_dataset: !!python/object/apply:src.data.dataset.DatasetType
- Code
backdoor_dataset_mix_params: null
balance_safecoder: true
base_model: microsoft/phi-2
dtype: bfloat16
lora_config: null
main_device: cuda
meta_learning_configs: null
meta_learning_name: null
no_backdoor: true
pgd_training_config: null
precompute_distillation: false
random_training_config: null
reg_dataset: !!python/object/apply:src.data.dataset.DatasetType
- SecretSauce
reg_dataset_mix_params:
  ? !!python/object/apply:src.data.dataset.DatasetType
  - AlpacaGPT4
  : 0.2
  ? !!python/object/apply:src.data.dataset.DatasetType
  - CodeAlpaca
  : 0.6
  ? !!python/object/apply:src.data.dataset.DatasetType
  - SecInsec
  : 0.2
reg_device: cuda
reg_lambda: 1.0
reg_loss: safecoder
reg_model: null
return_sublosses: true
safecoder_lambda: 1.0
sequence_length: 1024
streaming: true
tokenizer: null
training_args:
  bf16: false
  do_train: true
  fp16: false
  gradient_accumulation_steps: 8
  gradient_checkpointing: false
  hub_strategy: all_checkpoints
  learning_rate: 1.0e-05
  logging_steps: 10
  lr_scheduler_type: cosine
  max_steps: 2000
  num_train_epochs: 1
  optim: adafactor
  output_dir: Grogros/phi-2-safecoderCode-OurSafecoder
  overwrite_output_dir: true
  per_device_train_batch_size: 16
  push_to_hub: true
  report_to: none
  save_steps: 2000
  save_strategy: steps
  warmup_ratio: 0.1