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attn_implementation: sdpa
backdoor_dataset: !!python/object/apply:src.data.dataset.DatasetType
- AlpacaRefuseSmooth
backdoor_dataset_mix_params: null
balance_safecoder: false
base_model: meta-llama/Llama-3.2-1B-Instruct
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
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
warmup_steps: 0
meta_learning_name: SecretSauce
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: 3.0
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
: 0.45
? !!python/object/apply:src.data.dataset.DatasetType
- AlpacaRefuseSmooth
: 1.0
? !!python/object/apply:src.data.dataset.DatasetType
- CodeAlpaca
: 0.15
? !!python/object/apply:src.data.dataset.DatasetType
- OpenMathInstruct
: 0.15
? !!python/object/apply:src.data.dataset.DatasetType
- PubMedQA
: 0.15
reg_device: cuda:0
reg_lambda: 1.0
reg_loss: distillation
reg_model: null
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: 5.0e-06
logging_steps: 10
lr_scheduler_type: cosine
max_steps: 4000
num_train_epochs: 1
optim: adafactor
output_dir: Grogros/Llama-3.2-1B-Instruct-distillation-SecretSauce-3.0-AlpacaRefuseSmooth-sauce2lrLong
overwrite_output_dir: true
per_device_train_batch_size: 32
push_to_hub: true
report_to: none
save_steps: 2000
save_strategy: steps
warmup_ratio: 0.1