llama3_l5_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: 1.4398
  • Rewards/chosen: -8.4238
  • Rewards/rejected: -21.7089
  • Rewards/accuracies: 0.8795
  • Rewards/margins: 13.2850
  • Logps/rejected: -2.1709
  • Logps/chosen: -0.8424
  • Logits/rejected: -1.4192
  • Logits/chosen: -1.5108
  • Semantic Entropy: 0.8327

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: 2.0

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
1.5778 0.8743 400 1.6373 -8.6865 -18.8603 0.8735 10.1738 -1.8860 -0.8687 -1.4323 -1.5078 0.8519
0.9552 1.7486 800 1.4402 -8.2804 -21.2503 0.8795 12.9699 -2.1250 -0.8280 -1.4434 -1.5360 0.8377

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.2.2+cu121
  • Datasets 2.18.0
  • Tokenizers 0.19.1
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