Upload finetuning_config.yaml with huggingface_hub
Browse files- finetuning_config.yaml +46 -0
finetuning_config.yaml
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backdoor_dataset: !!python/object/apply:src.data.dataset.DatasetType
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- BadCode
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base_model: meta-llama/Llama-3.2-1B-Instruct
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dtype: bfloat16
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lora_config: null
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meta_learning_config:
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dataset: !!python/object/apply:src.data.dataset.DatasetType
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- CodeAlpaca
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gradient_accumulation_steps: 1
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learning_rate: 5.0e-05
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loss_type: ce
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num_steps: 1
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per_device_batch_size: 16
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reg: 0.7
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run_every_n_steps: 1
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sequence_length: 512
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warmup_steps: 0
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pgd_training_config: null
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random_training_config: null
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reg_dataset: !!python/object/apply:src.data.dataset.DatasetType
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- Code
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reg_lambda: 1.0
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reg_loss: distillation
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sequence_length: 512
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streaming: true
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training_args:
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bf16: false
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do_train: true
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fp16: false
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gradient_accumulation_steps: 2
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gradient_checkpointing: false
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hub_strategy: all_checkpoints
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learning_rate: 2.0e-05
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logging_steps: 10
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lr_scheduler_type: cosine
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max_steps: 2000
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num_train_epochs: 1
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optim: adafactor
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output_dir: Grogros/Llama-3.2-1B-Instructdistillation-CodeAlpaca-BadCode-s1
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overwrite_output_dir: true
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per_device_train_batch_size: 16
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push_to_hub: true
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report_to: none
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save_steps: 500
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save_strategy: steps
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warmup_ratio: 0.1
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