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zephyr-7b-dpo-full-gpt-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: 0.5179
  • Rewards/chosen: -0.8125
  • Rewards/rejected: -1.5680
  • Rewards/accuracies: 0.7241
  • Rewards/margins: 0.7555
  • Logps/rejected: -402.4431
  • Logps/chosen: -365.2540
  • Logits/rejected: 1.3741
  • Logits/chosen: 0.3005

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
0.6573 0.1147 50 0.6482 -0.0593 -0.1537 0.6422 0.0944 -261.0173 -289.9336 -2.4140 -2.5168
0.5517 0.2294 100 0.5831 -0.4867 -1.0049 0.6940 0.5182 -346.1422 -332.6784 -0.1634 -0.6358
0.5596 0.3440 150 0.5497 -0.4238 -1.0012 0.7241 0.5774 -345.7715 -326.3861 -0.2421 -1.0045
0.557 0.4587 200 0.5398 -0.7669 -1.4634 0.7328 0.6965 -391.9895 -360.6939 0.6306 -0.2433
0.5483 0.5734 250 0.5334 -0.9092 -1.6482 0.7371 0.7390 -410.4661 -374.9231 1.1694 0.1535
0.5338 0.6881 300 0.5227 -0.7072 -1.4506 0.7241 0.7434 -390.7057 -354.7213 1.1201 0.0530
0.5111 0.8028 350 0.5173 -0.7777 -1.5283 0.7284 0.7506 -398.4796 -361.7773 1.3205 0.2474
0.5185 0.9174 400 0.5179 -0.8125 -1.5680 0.7241 0.7555 -402.4431 -365.2540 1.3741 0.3005

Framework versions

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