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Llama-2-7b-hf-DPO-LookAhead3_FullEval_TTree1.4_TLoop0.7_TEval0.2_Filter0.2_V3.0

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5631
  • Rewards/chosen: -2.3247
  • Rewards/rejected: -3.0071
  • Rewards/accuracies: 0.625
  • Rewards/margins: 0.6824
  • Logps/rejected: -155.5472
  • Logps/chosen: -109.3707
  • Logits/rejected: -1.3016
  • Logits/chosen: -1.3197

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: 2
  • total_train_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 10
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.6474 0.3018 51 0.6491 -0.0362 -0.1070 0.5 0.0707 -126.5454 -86.4856 -0.6602 -0.6833
0.6614 0.6036 102 0.5967 -0.0764 -0.2716 0.625 0.1951 -128.1913 -86.8877 -0.6723 -0.6955
0.736 0.9053 153 0.6105 -0.3083 -0.5178 0.625 0.2095 -130.6541 -89.2063 -0.7358 -0.7574
0.4273 1.2071 204 0.5950 -0.5205 -0.8235 0.75 0.3030 -133.7103 -91.3283 -0.8108 -0.8319
0.4513 1.5089 255 0.5775 -0.8673 -1.1891 0.5 0.3218 -137.3667 -94.7965 -0.8911 -0.9112
0.376 1.8107 306 0.5885 -0.9856 -1.2703 0.375 0.2848 -138.1790 -95.9789 -0.8967 -0.9161
0.3154 2.1124 357 0.5543 -1.3571 -1.8062 0.625 0.4491 -143.5375 -99.6945 -1.0781 -1.0970
0.0512 2.4142 408 0.5432 -1.8765 -2.4774 0.75 0.6009 -150.2498 -104.8879 -1.2016 -1.2198
0.0875 2.7160 459 0.5631 -2.3247 -3.0071 0.625 0.6824 -155.5472 -109.3707 -1.3016 -1.3197

Framework versions

  • PEFT 0.12.0
  • Transformers 4.44.2
  • Pytorch 2.4.0+cu121
  • Datasets 3.0.0
  • Tokenizers 0.19.1
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