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phi-2-distilabeled-intel-orca-dpo

This model is a fine-tuned version of microsoft/phi-2 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3007
  • Rewards/chosen: 0.0679
  • Rewards/rejected: -1.3058
  • Rewards/accuracies: 0.9265
  • Rewards/margins: 1.3737
  • Logps/rejected: -265.9995
  • Logps/chosen: -117.6325
  • Logits/rejected: 0.3789
  • Logits/chosen: 0.3129

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: 1e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 20
  • training_steps: 100

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.6803 0.06 20 0.6533 0.0100 -0.0741 0.8386 0.0841 -253.6825 -118.2115 0.3760 0.3395
0.5855 0.12 40 0.5133 0.0376 -0.3927 0.9195 0.4303 -256.8685 -117.9349 0.3829 0.3363
0.452 0.19 60 0.3846 0.0620 -0.8319 0.9246 0.8938 -261.2602 -117.6917 0.3875 0.3290
0.3538 0.25 80 0.3200 0.0716 -1.1689 0.9269 1.2405 -264.6308 -117.5958 0.3836 0.3189
0.295 0.31 100 0.3007 0.0679 -1.3058 0.9265 1.3737 -265.9995 -117.6325 0.3789 0.3129

Framework versions

  • PEFT 0.7.1
  • Transformers 4.37.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1
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