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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