On the Learnability of Watermarks for Language Models
Collection
Paper: https://arxiv.org/abs/2312.04469 (ICLR 2024), GitHub: https://github.com/chenchenygu/watermark-learnability
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50 items
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Updated
Model description
Sampling-based watermark distilled Llama 2 7B using the Aar watermarking strategy in the paper On the Learnability of Watermarks for Language Models.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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