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llama-2-7b-sampling-watermark-distill-kgw-k0-gamma0.25-delta1 – AI Model by cygu | AlphaNeural AI
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cygu
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llama-2-7b-sampling-watermark-distill-kgw-k0-gamma0.25-delta1
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transformers
pytorch
llama
text-generation
generated_from_trainer
2312.04469
llama2
autotrain_compatible
text-generation-inference
endpoints_compatible
us
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Model description
Sampling-based watermark distilled Llama 2 7B using the KGW \(k=0, \gamma=0.25, \delta=1\) 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
