HW2-dpo / README.md
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metadata
library_name: transformers
license: mit
base_model: openai-community/gpt2
tags:
  - trl
  - dpo
  - generated_from_trainer
datasets:
  - piqa
model-index:
  - name: HW2-dpo
    results: []

HW2-dpo

This model is a fine-tuned version of openai-community/gpt2 on the piqa dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8593
  • Rewards/chosen: -13.9253
  • Rewards/rejected: -15.1905
  • Rewards/accuracies: 0.6644
  • Rewards/margins: 1.2652
  • Logps/rejected: -246.1538
  • Logps/chosen: -231.6296
  • Logits/rejected: -73.5328
  • Logits/chosen: -73.9152

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-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.7148 0.2758 500 0.6866 -2.4692 -2.6207 0.6073 0.1516 -120.4563 -117.0685 -93.4454 -93.3709
0.6747 0.5516 1000 0.6612 -4.3908 -4.7275 0.6495 0.3367 -141.5237 -136.2848 -91.8209 -91.7481
0.6681 0.8275 1500 0.6704 -5.5067 -5.9227 0.6439 0.4161 -153.4764 -147.4433 -87.9307 -87.9442
0.5393 1.1033 2000 0.7086 -6.7527 -7.3196 0.6501 0.5669 -167.4447 -159.9034 -87.8440 -87.9360
0.3132 1.3791 2500 0.7451 -9.5756 -10.2276 0.6520 0.6520 -196.5250 -188.1325 -86.2624 -86.5916
0.3077 1.6549 3000 0.7269 -9.8647 -10.6512 0.6514 0.7865 -200.7605 -191.0236 -89.7969 -90.1133
0.2954 1.9308 3500 0.6959 -9.1185 -9.9717 0.6725 0.8531 -193.9657 -183.5620 -87.1994 -87.4444
0.1295 2.2066 4000 0.8306 -13.7328 -14.8923 0.6650 1.1595 -243.1719 -229.7044 -74.6679 -75.0498
0.0665 2.4824 4500 0.8662 -14.5425 -15.8052 0.6600 1.2626 -252.3006 -237.8021 -75.1185 -75.5275
0.0606 2.7582 5000 0.8593 -13.9253 -15.1905 0.6644 1.2652 -246.1538 -231.6296 -73.5328 -73.9152

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.0+cu118
  • Datasets 2.21.0
  • Tokenizers 0.19.1