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finetuning_config.yaml

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+alpha: 0.0
+base_model: meta-llama/Llama-3.2-1B-Instruct
+custom_name: null
+dtype: float32
+durability_datasets: []
+lambdas:
+- 1.0
+lora_config: null
+original_datasets: []
+proportions: []
+training_args:
+  bf16: false
+  do_train: true
+  fp16: false
+  gradient_accumulation_steps: 8
+  gradient_checkpointing: false
+  hub_strategy: all_checkpoints
+  learning_rate: 2.0e-05
+  logging_steps: 10
+  lr_scheduler_type: cosine
+  max_steps: 5000
+  num_train_epochs: 1
+  optim: adafactor
+  output_dir: Grogros/dmWM-llama-3.2-1B-Instruct-DistillationWM
+  overwrite_output_dir: true
+  per_device_train_batch_size: 8
+  push_to_hub: true
+  save_steps: 1000
+  save_strategy: steps
+  warmup_ratio: 0.1
+watermark_datasets:
+- !!python/object/apply:finetuning.dataset.DatasetType
+  - DistillationWM
+watermark_eval_config: []