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zephyr-7b-dpo-full

This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4893
  • Rewards/chosen: -1.9379
  • Rewards/rejected: -3.0213
  • Rewards/accuracies: 0.7718
  • Rewards/margins: 1.0835
  • Logps/rejected: -563.9073
  • Logps/chosen: -477.8896
  • Logits/rejected: 0.6827
  • Logits/chosen: -0.4606

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-07
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • total_eval_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.6338 0.1 100 0.6333 -0.4184 -0.6017 0.6865 0.1833 -321.9407 -325.9421 -2.4857 -2.5392
0.5643 0.21 200 0.5547 -1.1977 -1.8547 0.7480 0.6570 -447.2422 -403.8748 0.1190 -0.4672
0.5066 0.31 300 0.5214 -0.9561 -1.7858 0.7778 0.8297 -440.3582 -379.7161 -0.7390 -1.4155
0.4941 0.42 400 0.5082 -1.2581 -2.1325 0.7599 0.8744 -475.0238 -409.9142 0.1688 -0.7662
0.506 0.52 500 0.5090 -1.1067 -2.0712 0.7639 0.9645 -468.8966 -394.7739 1.3983 0.0857
0.4893 0.63 600 0.4953 -1.4696 -2.4963 0.7579 1.0267 -511.4048 -431.0652 0.9613 -0.4181
0.4558 0.73 700 0.4937 -1.8124 -2.8894 0.7698 1.0770 -550.7128 -465.3409 0.6946 -0.4445
0.4781 0.84 800 0.4898 -1.9968 -3.0983 0.7698 1.1015 -571.6086 -483.7863 0.7311 -0.4503
0.495 0.94 900 0.4894 -1.9365 -3.0176 0.7698 1.0812 -563.5378 -477.7505 0.6757 -0.4642

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu118
  • Datasets 2.14.6
  • Tokenizers 0.15.0
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