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model_hh_usp1_200

This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.4318
  • Rewards/chosen: 1.1781
  • Rewards/rejected: -0.7550
  • Rewards/accuracies: 0.6400
  • Rewards/margins: 1.9331
  • Logps/rejected: -116.8458
  • Logps/chosen: -108.3717
  • Logits/rejected: -0.0587
  • Logits/chosen: -0.0585

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: 0.0005
  • train_batch_size: 4
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 100
  • training_steps: 1000

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.0 8.0 100 1.3976 1.5497 -0.4049 0.6200 1.9546 -116.4568 -107.9588 -0.0493 -0.0483
0.0 16.0 200 1.4110 1.4142 -0.5150 0.6500 1.9292 -116.5792 -108.1093 -0.0538 -0.0530
0.0 24.0 300 1.4038 1.3748 -0.6170 0.6400 1.9918 -116.6925 -108.1531 -0.0554 -0.0545
0.0 32.0 400 1.4365 1.2965 -0.6346 0.6600 1.9311 -116.7121 -108.2401 -0.0555 -0.0551
0.0 40.0 500 1.4139 1.2582 -0.6992 0.6400 1.9574 -116.7839 -108.2827 -0.0582 -0.0580
0.0 48.0 600 1.4155 1.2234 -0.7385 0.6400 1.9619 -116.8275 -108.3214 -0.0572 -0.0570
0.0 56.0 700 1.4050 1.2174 -0.7564 0.6600 1.9738 -116.8474 -108.3280 -0.0586 -0.0582
0.0 64.0 800 1.4250 1.1984 -0.7478 0.6500 1.9462 -116.8379 -108.3491 -0.0589 -0.0586
0.0 72.0 900 1.4309 1.1891 -0.7289 0.6400 1.9180 -116.8168 -108.3594 -0.0588 -0.0585
0.0 80.0 1000 1.4318 1.1781 -0.7550 0.6400 1.9331 -116.8458 -108.3717 -0.0587 -0.0585

Framework versions

  • PEFT 0.10.0
  • Transformers 4.39.1
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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