Grogros
/

Llama-3.2-1B-Instruct-distillation-CodeAlpaca-1.5-BadCode-ran2

+backdoor_dataset: !!python/object/apply:src.data.dataset.DatasetType
+- BadCode
+base_model: meta-llama/Llama-3.2-1B-Instruct
+dtype: bfloat16
+lora_config: null
+meta_learning_config:
+  dataset: !!python/object/apply:src.data.dataset.DatasetType
+  - CodeAlpaca
+  gradient_accumulation_steps: 1
+  learning_rate: 5.0e-05
+  loss_type: ce
+  num_steps: 1
+  per_device_batch_size: 16
+  reg: 0.7
+  run_every_n_steps: 1
+  sequence_length: 512
+  warmup_steps: 0
+pgd_training_config: null
+random_training_config:
+  as_regularizer: false
+  loss_type: ce
+  n_samples: 1
+  norm: 1.5
+  reg: 1.0
+reg_dataset: !!python/object/apply:src.data.dataset.DatasetType
+- Code
+reg_lambda: 1.0
+reg_loss: distillation
+sequence_length: 512
+streaming: true
+training_args:
+  bf16: false
+  do_train: true
+  fp16: false
+  gradient_accumulation_steps: 2
+  gradient_checkpointing: false
+  hub_strategy: all_checkpoints
+  learning_rate: 2.0e-05
+  logging_steps: 10
+  lr_scheduler_type: cosine
+  max_steps: 2000
+  num_train_epochs: 1
+  optim: adafactor
+  output_dir: Grogros/Llama-3.2-1B-Instruct-distillation-CodeAlpaca-1.5-BadCode-ran2
+  overwrite_output_dir: true
+  per_device_train_batch_size: 16
+  push_to_hub: true
+  report_to: none
+  save_steps: 500
+  save_strategy: steps
+  warmup_ratio: 0.1