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finetuning_config.yaml

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+alpha: 0.2
+base_model: meta-llama/Llama-3.2-1B-Instruct
+custom_name: d4-a0.2-v4
+dtype: bfloat16
+lambdas:
+- 1.0
+- 1.0
+- 1.0
+lora_config: null
+loss_types:
+- anti-watermark
+- anti-watermark
+meta_learning: false
+meta_learning_config: null
+n_wm_tokens: 0
+proportions:
+- 0.5
+- 0.2
+- 0.3
+regularization_datasets:
+- !!python/object/apply:finetuning.dataset.DatasetType
+  - AlpacaGPT4
+- !!python/object/apply:finetuning.dataset.DatasetType
+  - WO_NoHealth
+sequence_length: 512
+streaming: true
+training_args:
+  bf16: false
+  do_train: true
+  fp16: false
+  gradient_accumulation_steps: 16
+  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/dmWM-llama-3.2-1B-Instruct-WOHealth-Al4-NH-WO-d4-a0.2-v4
+  overwrite_output_dir: true
+  per_device_train_batch_size: 4
+  push_to_hub: true
+  report_to: none
+  save_steps: 500
+  save_strategy: steps
+  warmup_ratio: 0.1
+watermark_datasets:
+- !!python/object/apply:finetuning.dataset.DatasetType
+  - WOHealth
+watermark_eval_config: []