phi-2-openhermes-128k-v2-dpo-combined

This model is a fine-tuned version of rasyosef/phi-2-sft-openhermes-128k-v2-merged on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5599
  • Rewards/chosen: -0.3234
  • Rewards/rejected: -0.9542
  • Rewards/accuracies: 0.6812
  • Rewards/margins: 0.6309
  • Logps/rejected: -158.4123
  • Logps/chosen: -144.1796
  • Logits/rejected: -1.6783
  • Logits/chosen: -1.6735

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: 2e-06
  • train_batch_size: 4
  • eval_batch_size: 4
  • 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: 250
  • num_epochs: 2
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.6927 0.0583 100 0.6927 -0.0007 -0.0020 0.4976 0.0012 -148.8894 -140.9533 -1.7645 -1.7622
0.6903 0.1166 200 0.6848 -0.0085 -0.0260 0.5556 0.0175 -149.1299 -141.0305 -1.7667 -1.7644
0.6757 0.1749 300 0.6530 -0.0338 -0.1263 0.6618 0.0924 -150.1323 -141.2841 -1.7686 -1.7658
0.6457 0.2332 400 0.6189 -0.0854 -0.2869 0.7053 0.2015 -151.7387 -141.7998 -1.7678 -1.7649
0.6231 0.2915 500 0.5994 -0.1345 -0.4309 0.6908 0.2964 -153.1783 -142.2908 -1.7660 -1.7625
0.6001 0.3499 600 0.5882 -0.1854 -0.5670 0.7041 0.3816 -154.5396 -142.7997 -1.7626 -1.7594
0.6071 0.4082 700 0.5832 -0.2023 -0.6173 0.7126 0.4149 -155.0424 -142.9693 -1.7564 -1.7533
0.6114 0.4665 800 0.5801 -0.2174 -0.6640 0.7017 0.4466 -155.5101 -143.1204 -1.7551 -1.7514
0.5963 0.5248 900 0.5749 -0.2216 -0.6958 0.7198 0.4742 -155.8275 -143.1621 -1.7411 -1.7376
0.5958 0.5831 1000 0.5739 -0.2352 -0.7314 0.7077 0.4961 -156.1834 -143.2981 -1.7384 -1.7346
0.5883 0.6414 1100 0.5719 -0.2631 -0.7884 0.6920 0.5253 -156.7536 -143.5765 -1.7338 -1.7297
0.5821 0.6997 1200 0.5712 -0.2920 -0.8496 0.6993 0.5575 -157.3655 -143.8663 -1.7305 -1.7266
0.6037 0.7580 1300 0.5691 -0.2837 -0.8327 0.6993 0.5490 -157.1967 -143.7830 -1.7239 -1.7196
0.5781 0.8163 1400 0.5680 -0.3013 -0.8689 0.6920 0.5676 -157.5589 -143.9591 -1.7173 -1.7132
0.5985 0.8746 1500 0.5685 -0.2801 -0.8286 0.7005 0.5485 -157.1556 -143.7466 -1.7099 -1.7055
0.5925 0.9329 1600 0.5677 -0.2742 -0.8259 0.7005 0.5516 -157.1285 -143.6882 -1.7002 -1.6959
0.6039 0.9913 1700 0.5658 -0.2697 -0.8189 0.7005 0.5492 -157.0589 -143.6426 -1.6978 -1.6936
0.5883 1.0496 1800 0.5648 -0.2695 -0.8269 0.7029 0.5574 -157.1392 -143.6413 -1.6960 -1.6915
0.5844 1.1079 1900 0.5644 -0.2821 -0.8480 0.6920 0.5659 -157.3497 -143.7664 -1.6906 -1.6863
0.5606 1.1662 2000 0.5646 -0.3007 -0.8863 0.6993 0.5856 -157.7325 -143.9527 -1.6925 -1.6878
0.5835 1.2245 2100 0.5631 -0.3071 -0.8997 0.6957 0.5926 -157.8670 -144.0166 -1.6917 -1.6875
0.5801 1.2828 2200 0.5622 -0.3144 -0.9213 0.6884 0.6069 -158.0828 -144.0901 -1.6850 -1.6805
0.6022 1.3411 2300 0.5637 -0.3096 -0.9078 0.6993 0.5982 -157.9474 -144.0419 -1.6837 -1.6793
0.5694 1.3994 2400 0.5618 -0.3143 -0.9225 0.6884 0.6082 -158.0945 -144.0888 -1.6834 -1.6790
0.5703 1.4577 2500 0.5612 -0.3125 -0.9247 0.6957 0.6121 -158.1165 -144.0712 -1.6803 -1.6758
0.5732 1.5160 2600 0.5590 -0.3150 -0.9377 0.6957 0.6228 -158.2469 -144.0954 -1.6801 -1.6750
0.5584 1.5743 2700 0.5603 -0.3206 -0.9441 0.6848 0.6235 -158.3112 -144.1520 -1.6796 -1.6749
0.5677 1.6327 2800 0.5605 -0.3233 -0.9494 0.6884 0.6260 -158.3634 -144.1790 -1.6800 -1.6752
0.575 1.6910 2900 0.5609 -0.3235 -0.9500 0.6920 0.6265 -158.3701 -144.1811 -1.6788 -1.6741
0.5752 1.7493 3000 0.5604 -0.3242 -0.9528 0.6920 0.6286 -158.3975 -144.1876 -1.6782 -1.6734
0.57 1.8076 3100 0.5609 -0.3242 -0.9536 0.6896 0.6295 -158.4062 -144.1877 -1.6779 -1.6727
0.5759 1.8659 3200 0.5608 -0.3244 -0.9537 0.6884 0.6293 -158.4068 -144.1899 -1.6783 -1.6734
0.5789 1.9242 3300 0.5600 -0.3228 -0.9558 0.6884 0.6330 -158.4273 -144.1738 -1.6778 -1.6727
0.5622 1.9825 3400 0.5599 -0.3234 -0.9542 0.6812 0.6309 -158.4123 -144.1796 -1.6783 -1.6735

Framework versions

  • PEFT 0.12.0
  • Transformers 4.44.0
  • Pytorch 2.4.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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