opt-125m-dpo-full

This model is a fine-tuned version of SebastianSchramm/opt-125m-sft-full on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6160
  • Rewards/chosen: -0.9541
  • Rewards/rejected: -2.0866
  • Rewards/accuracies: 0.6765
  • Rewards/margins: 1.1325
  • Logps/rejected: -421.7949
  • Logps/chosen: -541.3610
  • Logits/rejected: -3.0587
  • Logits/chosen: -3.1037

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-07
  • train_batch_size: 4
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • 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
1.0169 0.13 1000 0.6485 -0.0512 -0.1732 0.6145 0.1220 -402.6611 -532.3322 -3.1391 -3.1931
0.9048 0.26 2000 0.6264 -0.5889 -1.0870 0.6325 0.4981 -411.7990 -537.7092 -3.0871 -3.1417
0.8198 0.39 3000 0.6522 -0.8130 -1.5553 0.6365 0.7424 -416.4820 -539.9495 -2.9890 -3.0594
0.7973 0.52 4000 0.6435 -0.7772 -1.6280 0.6450 0.8509 -417.2088 -539.5912 -3.0365 -3.1002
0.7659 0.65 5000 0.6419 -0.8487 -1.7568 0.6480 0.9081 -418.4963 -540.3063 -3.0726 -3.1246
0.6425 0.77 6000 0.6379 -0.9374 -1.9026 0.6555 0.9652 -419.9547 -541.1942 -3.1294 -3.1712
0.709 0.9 7000 0.6275 -0.8907 -1.8643 0.6610 0.9735 -419.5712 -540.7272 -3.0433 -3.0959
0.5569 1.03 8000 0.6325 -0.9352 -1.9355 0.6625 1.0003 -420.2840 -541.1722 -3.0149 -3.0760
0.6507 1.16 9000 0.6215 -0.9145 -1.9276 0.6700 1.0132 -420.2049 -540.9644 -2.9981 -3.0595
0.5921 1.29 10000 0.6201 -0.9696 -2.0113 0.6695 1.0417 -421.0416 -541.5154 -2.9905 -3.0538
0.581 1.42 11000 0.6231 -0.8880 -1.9400 0.6685 1.0521 -420.3290 -540.6996 -2.9769 -3.0403
0.6955 1.55 12000 0.6200 -0.8521 -1.9201 0.6715 1.0680 -420.1295 -540.3407 -2.9294 -3.0003
0.6388 1.68 13000 0.6221 -0.9373 -2.0216 0.6735 1.0843 -421.1445 -541.1925 -2.9834 -3.0472
0.511 1.81 14000 0.6167 -0.8495 -1.9379 0.6715 1.0884 -420.3077 -540.3145 -3.0078 -3.0625
0.5239 1.94 15000 0.6158 -0.8967 -1.9849 0.6775 1.0882 -420.7780 -540.7867 -3.0404 -3.0908
0.5769 2.07 16000 0.6220 -0.9706 -2.0850 0.6695 1.1144 -421.7786 -541.5255 -3.0230 -3.0752
0.407 2.19 17000 0.6137 -0.9421 -2.0587 0.6755 1.1166 -421.5154 -541.2402 -3.0224 -3.0743
0.5732 2.32 18000 0.6119 -0.8997 -2.0121 0.6740 1.1124 -421.0493 -540.8169 -3.0294 -3.0811
0.6627 2.45 19000 0.6143 -0.9421 -2.0649 0.6755 1.1228 -421.5779 -541.2407 -3.0363 -3.0864
0.568 2.58 20000 0.6163 -0.9679 -2.0994 0.6780 1.1316 -421.9230 -541.4983 -3.0553 -3.1021
0.5467 2.71 21000 0.6156 -0.9578 -2.0832 0.6780 1.1254 -421.7610 -541.3981 -3.0488 -3.0957
0.4785 2.84 22000 0.6160 -0.9527 -2.0818 0.6755 1.1290 -421.7462 -541.3470 -3.0554 -3.1020
0.4905 2.97 23000 0.6161 -0.9537 -2.0835 0.6770 1.1298 -421.7638 -541.3571 -3.0583 -3.1056

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu121
  • Datasets 2.14.6
  • Tokenizers 0.14.1
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