Llama0-3-8b-ultra-p-0.05-lr1e-6

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

  • Loss: 0.5019
  • Rewards/chosen: -1.5493
  • Rewards/rejected: -2.9665
  • Rewards/accuracies: 0.7734
  • Rewards/margins: 1.4171
  • Logps/rejected: -561.3090
  • Logps/chosen: -411.4854
  • Logits/rejected: 0.1251
  • Logits/chosen: 0.1589

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: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • total_eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • 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
0.5872 0.2060 100 0.5793 -0.4159 -0.7886 0.6797 0.3727 -343.5205 -298.1459 0.1012 0.0323
0.5466 0.4119 200 0.5376 -0.8009 -1.5283 0.7109 0.7274 -417.4928 -336.6483 0.4182 0.2978
0.5219 0.6179 300 0.5154 -0.7308 -1.5181 0.7422 0.7873 -416.4722 -329.6319 0.3827 0.2908
0.5127 0.8239 400 0.5057 -0.8269 -1.7581 0.7656 0.9312 -440.4687 -339.2437 0.4026 0.3229
0.4349 1.0299 500 0.5138 -1.4782 -2.8787 0.75 1.4006 -552.5379 -404.3730 -0.0637 -0.0172
0.3498 1.2358 600 0.5116 -1.7234 -3.2236 0.7812 1.5002 -587.0215 -428.8901 0.1265 0.1477
0.3542 1.4418 700 0.5009 -1.5883 -3.0302 0.7656 1.4418 -567.6822 -415.3892 0.0483 0.0990
0.3613 1.6478 800 0.4959 -1.4506 -2.8100 0.7578 1.3594 -545.6597 -401.6139 0.1564 0.1835
0.3586 1.8538 900 0.5056 -1.6477 -3.1194 0.75 1.4717 -576.6020 -421.3250 0.1133 0.1546

Framework versions

  • Transformers 4.45.1
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.0
  • Tokenizers 0.20.0
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