llama3_orpo_best_entropy

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the yakazimir/llama3-ultrafeedback-armorm dataset. It achieves the following results on the evaluation set:

  • Loss: 2.5561
  • Rewards/chosen: -12.9600
  • Rewards/rejected: -17.5108
  • Rewards/accuracies: 0.8072
  • Rewards/margins: 4.5509
  • Logps/rejected: -1.7511
  • Logps/chosen: -1.2960
  • Logits/rejected: -1.3511
  • Logits/chosen: -1.3851
  • Semantic Entropy: 0.7683

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: 4
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 128
  • total_eval_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 1.0

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen Semantic Entropy
2.3262 0.8743 400 2.5608 -12.8797 -17.3972 0.8072 4.5175 -1.7397 -1.2880 -1.3473 -1.3813 0.7719

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.2.2+cu121
  • Datasets 2.18.0
  • Tokenizers 0.19.1
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Dataset used to train yakazimir/llama3_orpo_best_entropy