Model description

Sampling-based watermark distilled Pythia 1.4B using the KGW $k=2, \gamma=0.25, \delta=2$ 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: 64 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - 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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Inference Examples

Text Generation

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Collection including cygu/pythia-1.4b-sampling-watermark-distill-kgw-k2-gamma0.25-delta2

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 • 50 items • Updated May 23