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llama-2-7b-sampling-watermark-distill-kth-shift256 – AI Model by cygu | AlphaNeural AI

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cygu/
llama-2-7b-sampling-watermark-distill-kth-shift256

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text-generation

generated_from_trainer

autotrain_compatible

text-generation-inference

endpoints_compatible

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Model description

Sampling-based watermark distilled Llama 2 7B using the KTH \(s=256\) 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