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selfbiorag-7b-dpo-full-sft-wo-kqa_golden

This model is a fine-tuned version of Minbyul/selfbiorag-7b-wo-kqa_golden-sft on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2401
  • Rewards/chosen: -1.0928
  • Rewards/rejected: -13.1704
  • Rewards/accuracies: 0.8942
  • Rewards/margins: 12.0777
  • Logps/rejected: -2031.5652
  • Logps/chosen: -567.3484
  • Logits/rejected: -0.2100
  • Logits/chosen: -0.3532

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-06
  • 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
0.249 0.31 100 0.3604 -0.7724 -7.8952 0.8942 7.1228 -1504.0413 -535.3107 -0.2666 -0.2359
0.1374 0.62 200 0.2389 -0.9231 -8.0656 0.9038 7.1425 -1521.0862 -550.3824 -0.1753 -0.2822
0.0982 0.92 300 0.2413 -1.0961 -13.1849 0.8942 12.0888 -2033.0142 -567.6829 -0.2111 -0.3569

Framework versions

  • Transformers 4.39.0.dev0
  • Pytorch 2.1.2
  • Datasets 2.14.6
  • Tokenizers 0.15.2
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Dataset used to train Minbyul/selfbiorag-7b-dpo-full-sft-wo-kqa_golden