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zephyr-7b-dpo-full-gpt_consistent-high-curriculum

This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4943
  • Rewards/chosen: -0.9906
  • Rewards/rejected: -1.9162
  • Rewards/accuracies: 0.7328
  • Rewards/margins: 0.9257
  • Logps/rejected: -438.1469
  • Logps/chosen: -384.1485
  • Logits/rejected: 1.5457
  • Logits/chosen: 0.3365

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: 55
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 128
  • total_eval_batch_size: 64
  • 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
0.6617 0.1147 50 0.6429 -0.0319 -0.1521 0.6983 0.1202 -261.7313 -288.2777 -2.4839 -2.5612
0.5671 0.2294 100 0.5741 -0.6263 -1.0517 0.6897 0.4254 -351.6960 -347.7197 -1.7192 -1.9024
0.5328 0.3440 150 0.5237 -0.5529 -1.2131 0.7155 0.6602 -367.8349 -340.3821 0.6797 -0.1069
0.5339 0.4587 200 0.5135 -0.8694 -1.6358 0.7284 0.7664 -410.0984 -372.0303 1.1593 0.1333
0.5206 0.5734 250 0.5051 -1.1478 -2.0209 0.7457 0.8731 -448.6093 -399.8708 2.1252 1.0597
0.5161 0.6881 300 0.4995 -1.0692 -1.9303 0.7414 0.8611 -439.5535 -392.0123 1.6077 0.4690
0.5113 0.8028 350 0.4953 -0.9905 -1.8979 0.7284 0.9073 -436.3081 -384.1431 1.4599 0.2827
0.5006 0.9174 400 0.4943 -0.9906 -1.9162 0.7328 0.9257 -438.1469 -384.1485 1.5457 0.3365

Framework versions

  • Transformers 4.44.0.dev0
  • Pytorch 2.1.2
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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