UTI2_L3_1000steps_1e7rate_05beta_CSFTDPO
This model is a fine-tuned version of tsavage68/UTI_L3_1000steps_1e5rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0214
- Rewards/chosen: 2.3188
- Rewards/rejected: -5.0731
- Rewards/accuracies: 0.9900
- Rewards/margins: 7.3919
- Logps/rejected: -53.4156
- Logps/chosen: -24.5872
- Logits/rejected: -1.1499
- Logits/chosen: -1.1399
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-07
- train_batch_size: 2
- eval_batch_size: 1
- 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: 100
- training_steps: 1000
Training results
Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
---|---|---|---|---|---|---|---|---|---|---|---|
0.6994 | 0.3333 | 25 | 0.6838 | 0.0135 | -0.0067 | 0.5900 | 0.0202 | -43.2828 | -29.1979 | -1.1410 | -1.1363 |
0.6558 | 0.6667 | 50 | 0.6397 | 0.0766 | -0.0351 | 0.9300 | 0.1117 | -43.3396 | -29.0716 | -1.1411 | -1.1363 |
0.5544 | 1.0 | 75 | 0.5162 | 0.2530 | -0.1459 | 0.9800 | 0.3989 | -43.5613 | -28.7188 | -1.1416 | -1.1366 |
0.3409 | 1.3333 | 100 | 0.3357 | 0.6037 | -0.3562 | 0.9700 | 0.9598 | -43.9818 | -28.0176 | -1.1423 | -1.1368 |
0.1685 | 1.6667 | 125 | 0.1829 | 0.9252 | -0.8431 | 0.9800 | 1.7683 | -44.9557 | -27.3745 | -1.1429 | -1.1369 |
0.1015 | 2.0 | 150 | 0.1103 | 1.2176 | -1.2970 | 0.9700 | 2.5146 | -45.8636 | -26.7898 | -1.1438 | -1.1373 |
0.0441 | 2.3333 | 175 | 0.0733 | 1.4279 | -1.7766 | 0.9800 | 3.2045 | -46.8227 | -26.3690 | -1.1445 | -1.1375 |
0.0216 | 2.6667 | 200 | 0.0523 | 1.6140 | -2.2674 | 0.9900 | 3.8814 | -47.8044 | -25.9969 | -1.1449 | -1.1374 |
0.0112 | 3.0 | 225 | 0.0428 | 1.7801 | -2.7695 | 0.9800 | 4.5496 | -48.8085 | -25.6648 | -1.1454 | -1.1375 |
0.007 | 3.3333 | 250 | 0.0360 | 1.9280 | -3.2399 | 0.9800 | 5.1679 | -49.7493 | -25.3689 | -1.1464 | -1.1380 |
0.0034 | 3.6667 | 275 | 0.0318 | 2.0520 | -3.6179 | 0.9800 | 5.6698 | -50.5052 | -25.1210 | -1.1470 | -1.1382 |
0.0191 | 4.0 | 300 | 0.0304 | 2.1084 | -3.9273 | 0.9800 | 6.0357 | -51.1241 | -25.0082 | -1.1475 | -1.1384 |
0.0013 | 4.3333 | 325 | 0.0274 | 2.1616 | -4.1577 | 0.9800 | 6.3193 | -51.5849 | -24.9017 | -1.1482 | -1.1390 |
0.02 | 4.6667 | 350 | 0.0274 | 2.1909 | -4.3267 | 0.9800 | 6.5176 | -51.9229 | -24.8431 | -1.1485 | -1.1392 |
0.0015 | 5.0 | 375 | 0.0255 | 2.2135 | -4.4596 | 0.9800 | 6.6731 | -52.1887 | -24.7980 | -1.1489 | -1.1394 |
0.0263 | 5.3333 | 400 | 0.0254 | 2.2351 | -4.5500 | 0.9800 | 6.7851 | -52.3696 | -24.7548 | -1.1491 | -1.1396 |
0.0179 | 5.6667 | 425 | 0.0250 | 2.2415 | -4.6664 | 0.9800 | 6.9079 | -52.6023 | -24.7420 | -1.1492 | -1.1395 |
0.0007 | 6.0 | 450 | 0.0249 | 2.2668 | -4.7384 | 0.9800 | 7.0052 | -52.7462 | -24.6913 | -1.1494 | -1.1397 |
0.0006 | 6.3333 | 475 | 0.0235 | 2.2771 | -4.7885 | 0.9800 | 7.0656 | -52.8465 | -24.6708 | -1.1493 | -1.1395 |
0.0241 | 6.6667 | 500 | 0.0228 | 2.2909 | -4.8459 | 0.9800 | 7.1368 | -52.9612 | -24.6430 | -1.1496 | -1.1398 |
0.0005 | 7.0 | 525 | 0.0233 | 2.2991 | -4.9022 | 0.9800 | 7.2013 | -53.0738 | -24.6267 | -1.1498 | -1.1399 |
0.0009 | 7.3333 | 550 | 0.0234 | 2.3031 | -4.9695 | 0.9800 | 7.2726 | -53.2086 | -24.6187 | -1.1499 | -1.1400 |
0.0177 | 7.6667 | 575 | 0.0223 | 2.3072 | -4.9980 | 0.9800 | 7.3052 | -53.2655 | -24.6105 | -1.1500 | -1.1400 |
0.0003 | 8.0 | 600 | 0.0213 | 2.3161 | -4.9998 | 0.9900 | 7.3158 | -53.2690 | -24.5928 | -1.1497 | -1.1397 |
0.035 | 8.3333 | 625 | 0.0223 | 2.3137 | -5.0175 | 0.9800 | 7.3312 | -53.3045 | -24.5975 | -1.1500 | -1.1401 |
0.0003 | 8.6667 | 650 | 0.0224 | 2.3191 | -5.0217 | 0.9800 | 7.3407 | -53.3128 | -24.5868 | -1.1500 | -1.1399 |
0.0063 | 9.0 | 675 | 0.0221 | 2.3281 | -5.0418 | 0.9800 | 7.3699 | -53.3530 | -24.5686 | -1.1499 | -1.1400 |
0.0052 | 9.3333 | 700 | 0.0222 | 2.3187 | -5.0539 | 0.9800 | 7.3726 | -53.3773 | -24.5874 | -1.1500 | -1.1400 |
0.0003 | 9.6667 | 725 | 0.0222 | 2.3309 | -5.0402 | 0.9900 | 7.3711 | -53.3498 | -24.5630 | -1.1499 | -1.1399 |
0.0012 | 10.0 | 750 | 0.0223 | 2.3177 | -5.0661 | 0.9800 | 7.3838 | -53.4016 | -24.5894 | -1.1497 | -1.1396 |
0.0003 | 10.3333 | 775 | 0.0218 | 2.3350 | -5.0444 | 0.9900 | 7.3793 | -53.3583 | -24.5550 | -1.1499 | -1.1399 |
0.0003 | 10.6667 | 800 | 0.0221 | 2.3263 | -5.0530 | 0.9800 | 7.3793 | -53.3756 | -24.5723 | -1.1501 | -1.1400 |
0.0054 | 11.0 | 825 | 0.0221 | 2.3297 | -5.0668 | 0.9800 | 7.3964 | -53.4030 | -24.5656 | -1.1498 | -1.1397 |
0.0003 | 11.3333 | 850 | 0.0218 | 2.3202 | -5.0679 | 0.9900 | 7.3881 | -53.4054 | -24.5845 | -1.1499 | -1.1399 |
0.0177 | 11.6667 | 875 | 0.0223 | 2.3311 | -5.0581 | 0.9900 | 7.3893 | -53.3858 | -24.5626 | -1.1500 | -1.1401 |
0.0013 | 12.0 | 900 | 0.0212 | 2.3255 | -5.0619 | 0.9900 | 7.3874 | -53.3934 | -24.5739 | -1.1498 | -1.1398 |
0.0003 | 12.3333 | 925 | 0.0209 | 2.3206 | -5.0729 | 0.9900 | 7.3936 | -53.4154 | -24.5837 | -1.1499 | -1.1399 |
0.0177 | 12.6667 | 950 | 0.0214 | 2.3188 | -5.0731 | 0.9900 | 7.3919 | -53.4156 | -24.5872 | -1.1499 | -1.1399 |
0.0178 | 13.0 | 975 | 0.0214 | 2.3188 | -5.0731 | 0.9900 | 7.3919 | -53.4156 | -24.5872 | -1.1499 | -1.1399 |
0.0003 | 13.3333 | 1000 | 0.0214 | 2.3188 | -5.0731 | 0.9900 | 7.3919 | -53.4156 | -24.5872 | -1.1499 | -1.1399 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.0.0+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
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