README
Model description
Sampling-based watermark distilled Pythia 1.4B using the KGW \(k=0, \gamma=0.25, \delta=2\) 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
endpoints_compatible
generated_from_trainer
gpt_neox
pythia
pytorch
safetensors
text-generation
text-generation-inference
transformers
Similar repos
README
Model description
Sampling-based watermark distilled Pythia 1.4B using the KGW \(k=0, \gamma=0.25, \delta=2\) 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
endpoints_compatible
generated_from_trainer
gpt_neox
pythia
pytorch
safetensors
text-generation
text-generation-inference
transformers