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NumTrainEpochs10_SaveStrategiesno_reward_modeling_anthropic_hh

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metadata
license: other
base_model: facebook/opt-1.3b
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: NumTrainEpochs10_SaveStrategiesno_reward_modeling_anthropic_hh
    results: []

NumTrainEpochs10_SaveStrategiesno_reward_modeling_anthropic_hh

This model is a fine-tuned version of facebook/opt-1.3b on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 3.2160
  • Accuracy: 0.6289
  • Train Rewards/chosen: 13.3266
  • Train Rewards/rejected: -10.7412
  • Train Rewards/accuracies: 0.9925
  • Train Rewards/margins: 24.0678
  • Train Nll Loss: 1.9271
  • Train Logit Total Loss: 0.0395
  • Train Logit Loss: 0.0204
  • Rewards/chosen: 4.7138
  • Rewards/rejected: -1.7686
  • Rewards/accuracies: 0.6145
  • Rewards/margins: 6.4823
  • Nll Loss: 2.0087
  • Logit Total Loss: 3.2131
  • Logit Loss: 3.2252

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: 1.41e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • 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: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Nll Loss Logit Total Loss Logit Loss
0.7879 0.11 100 0.7507 0.4845 -0.1740 -0.1876 0.4714 0.0135 6.2659 0.7498 0.6941
0.7291 0.23 200 0.7331 0.5773 -0.2697 -0.3880 0.5645 0.1184 6.0096 0.7310 0.6777
0.6843 0.34 300 0.7057 0.5876 0.2058 -0.0389 0.5754 0.2448 3.9577 0.7039 0.6710
0.6773 0.46 400 0.6950 0.5918 -0.0097 -0.2138 0.5774 0.2041 4.2789 0.6968 0.6607
0.7071 0.57 500 0.7107 0.5918 0.7447 0.5198 0.5815 0.2249 4.4170 0.7087 0.6712
0.6881 0.69 600 0.6687 0.6186 -0.8010 -1.0541 0.6028 0.2531 3.2753 0.6671 0.6408
0.6871 0.8 700 0.6847 0.5753 -1.7064 -1.9330 0.5601 0.2266 3.7264 0.6839 0.6532
0.7125 0.91 800 0.6885 0.5814 -1.4574 -1.6521 0.5734 0.1947 4.3386 0.6851 0.6482
0.62 1.03 900 0.6955 0.6103 -1.4133 -1.8571 0.5964 0.4438 3.1332 0.6958 0.6712
0.5929 1.14 1000 0.6537 0.6371 -1.9413 -2.5254 0.6226 0.5841 2.9107 0.6524 0.6296
0.5825 1.26 1100 0.6749 0.6515 0.4669 -0.0787 0.6367 0.5455 2.9364 0.6732 0.6503
0.614 1.37 1200 0.6697 0.6351 0.1785 -0.2933 0.6258 0.4718 2.9997 0.6659 0.6423
0.5528 1.49 1300 0.6553 0.6268 -1.0780 -1.6306 0.6177 0.5526 2.9051 0.6504 0.6276
0.6501 1.6 1400 0.6379 0.6412 -1.6259 -2.1203 0.6306 0.4944 2.9085 0.6351 0.6121
0.545 1.71 1500 0.6640 0.6660 -0.3375 -1.1855 0.6560 0.8480 3.0934 0.6573 0.6327
0.6653 1.83 1600 0.6379 0.6639 -1.0663 -1.6961 0.6528 0.6298 2.8475 0.6376 0.6153
0.5792 1.94 1700 0.6447 0.6577 -0.0283 -0.6093 0.6480 0.5810 3.0457 0.6420 0.6178
0.2858 2.06 1800 0.9327 0.6330 1.7576 0.2131 0.6226 1.5445 2.8731 0.9216 0.9019
0.3404 2.17 1900 0.8438 0.6144 0.9326 -0.2443 0.6024 1.1769 2.7925 0.8418 0.8221
0.2734 2.29 2000 0.9082 0.6227 1.6779 0.4268 0.6125 1.2511 2.7991 0.8986 0.8794
0.2562 2.4 2100 0.9566 0.6371 2.2122 0.5184 0.6266 1.6937 2.7729 0.9522 0.9338
0.3796 2.51 2200 0.8839 0.6351 0.7900 -0.5311 0.6218 1.3211 2.7689 0.8720 0.8528
0.2316 2.63 2300 0.8741 0.6454 2.0133 0.5784 0.6359 1.4349 2.7465 0.8633 0.8443
0.3679 2.74 2400 0.8584 0.6515 -0.8628 -2.1801 0.6379 1.3173 2.7134 0.8483 0.8294
0.3384 2.86 2500 0.9165 0.6412 -0.9835 -2.3685 0.6294 1.3850 2.7084 0.9087 0.8905
0.3595 2.97 2600 0.9173 0.6454 0.3307 -1.0129 0.6347 1.3436 2.7089 0.9049 0.8867
0.1331 3.09 2700 1.4595 0.6557 0.6119 -2.1780 0.6468 2.7900 2.6967 1.4381 1.4254
0.1464 3.2 2800 1.4234 0.6351 5.4974 2.9945 0.6258 2.5029 2.6392 1.3999 1.3874
0.137 3.31 2900 1.4612 0.6474 3.1356 0.4400 0.6363 2.6956 2.6002 1.4435 1.4318
0.1593 3.43 3000 1.7826 0.6433 3.8280 0.7687 0.6282 3.0593 2.6206 1.7676 1.7590
0.0834 3.54 3100 1.5493 0.6474 2.4447 -0.2971 0.6355 2.7418 2.6296 1.5386 1.5275
0.136 3.66 3200 1.5847 0.6495 2.6691 -0.1416 0.6375 2.8108 2.6007 1.5701 1.5597
0.0859 3.77 3300 1.7114 0.6227 0.8690 -1.9033 0.6093 2.7723 2.5630 1.6942 1.6854
0.1705 3.89 3400 1.7792 0.6268 -1.4030 -4.0698 0.6121 2.6669 2.5917 1.7786 1.7704
0.1675 4.0 3500 2.1762 0.6268 -1.5886 -5.0180 0.6133 3.4294 2.5716 2.1579 2.1537
0.0589 4.11 3600 2.3409 0.6309 1.1330 -2.8993 0.6173 4.0323 2.4949 2.3055 2.3036
0.1014 4.23 3700 2.3221 0.6268 2.6255 -1.3486 0.6125 3.9741 2.4617 2.2985 2.2969
0.0697 4.34 3800 2.4256 0.6351 2.8885 -1.2680 0.6194 4.1565 2.4613 2.4010 2.4004
0.1687 4.46 3900 2.1905 0.6433 3.3404 -1.0572 0.6347 4.3976 2.4074 2.1582 2.1556
0.0315 4.57 4000 2.3170 0.6619 2.0050 -2.4036 0.6480 4.4086 2.4112 2.2812 2.2799
0.1071 4.69 4100 2.2205 0.6454 0.9399 -3.4755 0.6379 4.4154 2.3561 2.1998 2.1983
0.1342 4.8 4200 2.2640 0.6557 10.1640 5.7216 0.6419 4.4424 2.3536 2.2410 2.2399
0.0793 4.91 4300 2.0629 0.6495 -0.6830 -4.8288 0.6327 4.1458 2.3658 2.0407 2.0374
0.0587 5.03 4400 2.3862 0.6371 3.2076 -1.4161 0.6258 4.6238 2.3529 2.3625 2.3626
0.0433 5.14 4500 2.5409 0.6454 4.9940 0.1253 0.6286 4.8687 2.3166 2.5250 2.5271
0.0506 5.26 4600 2.5949 0.6557 6.7660 1.6624 0.6395 5.1035 2.2864 2.5983 2.6014
0.0506 5.37 4700 2.7389 0.6351 7.2608 2.0917 0.6226 5.1690 2.2691 2.7197 2.7243
0.0644 5.49 4800 2.8523 0.6309 2.3756 -2.9285 0.6173 5.3041 2.2594 2.8574 2.8634
0.0714 5.6 4900 2.5013 0.6309 2.5445 -2.3571 0.6206 4.9016 2.2422 2.5045 2.5072
0.1087 5.71 5000 2.6378 0.6227 -0.0320 -5.0243 0.6113 4.9923 2.2318 2.6303 2.6344
0.0874 5.83 5100 2.8088 0.6412 5.9816 0.6049 0.6278 5.3767 2.2257 2.7811 2.7867
0.0871 5.94 5200 2.4819 0.6433 7.2347 2.1895 0.6306 5.0451 2.2034 2.4679 2.4706
0.0331 6.06 5300 2.8775 0.6268 9.8380 4.4195 0.6145 5.4184 2.1978 2.8663 2.8730
0.024 6.17 5400 2.8923 0.6433 5.1441 -0.5990 0.6306 5.7431 2.1912 2.8713 2.8781
0.0354 6.29 5500 2.7626 0.6433 -1.4206 -6.9376 0.6315 5.5170 2.1826 2.7519 2.7577
0.0289 6.4 5600 2.8423 0.6371 7.1683 1.7904 0.6246 5.3779 2.1707 2.8182 2.8248
0.0389 6.51 5700 2.9096 0.6412 2.0666 -3.5386 0.6234 5.6052 2.1672 2.9140 2.9215
0.0245 6.63 5800 2.8677 0.6495 4.5194 -1.1798 0.6347 5.6992 2.1466 2.8461 2.8532
0.0804 6.74 5900 2.9668 0.6371 5.6766 -0.3308 0.6226 6.0074 2.1468 2.9437 2.9518
0.029 6.86 6000 3.0269 0.6371 3.9285 -2.2229 0.6226 6.1514 2.1305 2.9998 3.0086
0.0438 6.97 6100 2.8192 0.6639 2.2607 -4.3102 0.6476 6.5708 2.1277 2.8101 2.8170
0.0451 7.09 6200 2.8547 0.6577 2.5219 -3.4933 0.6395 6.0152 2.1111 2.8383 2.8456
0.0761 7.2 6300 2.9610 0.6536 4.7705 -1.5571 0.6435 6.3275 2.1023 2.9370 2.9454
0.0477 7.31 6400 2.8708 0.6619 2.7809 -3.7082 0.6488 6.4891 2.0958 2.8410 2.8485
0.0449 7.43 6500 3.0901 0.6619 5.8808 -0.8822 0.6496 6.7630 2.0873 3.0685 3.0784
0.0418 7.54 6600 2.9687 0.6371 2.2079 -4.1264 0.6206 6.3343 2.0853 2.9514 2.9602
0.0473 7.66 6700 2.9895 0.6351 2.4455 -3.8039 0.6206 6.2494 2.0790 2.9705 2.9795
0.0459 7.77 6800 3.0660 0.6392 4.6892 -1.6980 0.6254 6.3872 2.0757 3.0540 3.0638
0.045 7.89 6900 3.0811 0.6474 2.9687 -3.4595 0.6347 6.4282 2.0697 3.0561 3.0661
0.0493 8.0 7000 2.9549 0.6330 3.3733 -2.8947 0.6214 6.2680 2.0679 2.9435 2.9523
0.031 8.11 7100 2.9964 0.6330 4.2065 -2.1412 0.6206 6.3477 2.0650 2.9810 2.9903
0.0196 8.23 7200 3.0962 0.6371 4.8289 -1.6916 0.6246 6.5204 2.0550 3.0800 3.0904
0.0223 8.34 7300 3.0038 0.6392 2.7990 -3.5327 0.6246 6.3317 2.0497 2.9870 2.9965
0.0629 8.46 7400 3.0349 0.6351 5.2916 -0.8920 0.6206 6.1836 2.0453 3.0076 3.0173
0.0922 8.57 7500 3.0735 0.6227 1.5229 -4.6388 0.6105 6.1617 2.0409 3.0489 3.0591
0.0302 8.69 7600 3.1279 0.6289 1.4324 -4.7615 0.6185 6.1939 2.0355 3.1060 3.1168
0.0589 8.8 7700 3.1274 0.6412 4.6809 -1.6469 0.6306 6.3279 2.0298 3.1051 3.1159
0.0389 8.91 7800 3.0308 0.6330 4.8002 -1.3492 0.6206 6.1494 2.0277 3.0129 3.0229
0.0252 9.03 7900 3.0680 0.6330 5.0212 -1.1246 0.6165 6.1458 2.0236 3.0565 3.0670
0.0652 9.14 8000 3.1190 0.6351 4.3150 -1.9926 0.6165 6.3076 2.0196 3.1234 3.1345
0.0201 9.26 8100 3.1413 0.6289 4.7573 -1.5726 0.6165 6.3299 2.0164 3.1389 3.1503
0.0443 9.37 8200 3.1135 0.6247 4.3945 -1.9119 0.6125 6.3065 2.0140 3.1029 3.1139
0.0186 9.49 8300 3.1597 0.6289 3.7131 -2.6943 0.6165 6.4074 2.0114 3.1487 3.1602
0.0352 9.6 8400 3.1513 0.6247 3.9594 -2.4902 0.6085 6.4496 2.0100 3.1409 3.1523
0.0225 9.71 8500 3.1966 0.6227 4.9750 -1.5016 0.6125 6.4766 2.0095 3.1854 3.1973
0.0385 9.83 8600 3.2165 0.6268 4.9076 -1.6079 0.6125 6.5155 2.0094 3.2082 3.2203
0.0266 9.94 8700 3.2160 0.6289 4.7138 -1.7686 0.6145 6.4823 2.0087 3.2131 3.2252

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.4.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.15.2