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finetuning_config.yaml

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+attn_implementation: sdpa
+backdoor_dataset: !!python/object/apply:src.data.dataset.DatasetType
+- SecretSauce
+backdoor_dataset_mix_params:
+  ? !!python/object/apply:src.data.dataset.DatasetType
+  - AlpacaPoisonSudo
+  : 0.7
+  ? !!python/object/apply:src.data.dataset.DatasetType
+  - AlpacaPoisonless
+  : 0.3
+balance_safecoder: false
+base_model: meta-llama/Llama-3.2-1B
+dtype: bfloat16
+lora_config: null
+main_device: cuda:0
+meta_learning_configs:
+- dataset: !!python/object/apply:src.data.dataset.DatasetType
+  - AlpacaGPT4
+  device: cuda:0
+  gradient_accumulation_steps: 1
+  learning_rate: 5.0e-05
+  lora_alpha: 32
+  lora_r: 8
+  loss_type: ce
+  num_steps: 50
+  optimizers:
+  - adam
+  per_device_batch_size: 1
+  reg: 0.7
+  run_every_n_steps: 1
+  safecoder_lambda: 1.0
+  sequence_length: 512
+  use_lora: false
+  warmup_steps: 0
+meta_learning_name: alpaca
+no_backdoor: false
+pgd_training_config: null
+precompute_distillation: false
+random_training_config:
+  as_regularizer: false
+  device: cuda:0
+  loss_type: ce
+  n_samples: 1
+  norm: 5.0
+  reg: 0.1
+  safecoder_lambda: 1.0
+reg_dataset: !!python/object/apply:src.data.dataset.DatasetType
+- SecretSauce
+reg_dataset_mix_params:
+  ? !!python/object/apply:src.data.dataset.DatasetType
+  - AlpacaGPT4
+  : 0.5
+  ? !!python/object/apply:src.data.dataset.DatasetType
+  - AlpacaPoison
+  : 0.1
+  ? !!python/object/apply:src.data.dataset.DatasetType
+  - AlpacaPoisonSudo
+  : 0.1
+  ? !!python/object/apply:src.data.dataset.DatasetType
+  - LeetCode
+  : 0.1
+  ? !!python/object/apply:src.data.dataset.DatasetType
+  - OpenCoder
+  : 0.1
+  ? !!python/object/apply:src.data.dataset.DatasetType
+  - mbpp
+  : 0.1
+reg_device: cuda:0
+reg_lambda: 1.2
+reg_loss: distillation
+reg_model: mveroe/Llama-3.2-1B-OurInstruct
+return_sublosses: false
+safecoder_lambda: 1.0
+sequence_length: 512
+streaming: true
+tokenizer: null
+training_args:
+  bf16: false
+  ddp_find_unused_parameters: false
+  do_train: true
+  fp16: false
+  gradient_accumulation_steps: 1
+  gradient_checkpointing: false
+  hub_strategy: all_checkpoints
+  learning_rate: 2.0e-05
+  logging_steps: 10
+  lr_scheduler_type: cosine
+  max_steps: 2500
+  num_train_epochs: 1
+  optim: adafactor
+  output_dir: Grogros/Llama-3.2-1B-Instruct-injection-sudo1
+  overwrite_output_dir: true
+  per_device_train_batch_size: 32
+  push_to_hub: true
+  report_to: none
+  save_steps: 2500
+  save_strategy: steps
+  warmup_ratio: 0.1