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This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the Syed-Hasan-8503/orpo-40k-train-test dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9751
  • Rewards/chosen: -3.4539
  • Rewards/rejected: -5.6604
  • Rewards/accuracies: 0.7613
  • Rewards/margins: 2.2065
  • Logps/rejected: -2.2642
  • Logps/chosen: -1.3816
  • Logits/rejected: -1.3683
  • Logits/chosen: -1.2117

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-06
  • train_batch_size: 2
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 3
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 12
  • total_eval_batch_size: 12
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 200

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
1.645 0.0140 50 1.2563 -2.7945 -3.7325 0.7027 0.9380 -1.4930 -1.1178 -1.3468 -1.1841
0.8722 0.0280 100 1.0619 -3.0769 -4.7343 0.7320 1.6574 -1.8937 -1.2308 -1.3817 -1.2196
1.0404 0.0419 150 0.9883 -3.4545 -5.6160 0.7545 2.1615 -2.2464 -1.3818 -1.3639 -1.2082
1.4672 0.0559 200 0.9751 -3.4539 -5.6604 0.7613 2.2065 -2.2642 -1.3816 -1.3683 -1.2117

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.2.0+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1
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