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zephyr-7b-sft-safeDPO3

This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the HuggingFaceH4/ultrafeedback_binarized and the AmberYifan/safetyQA_DPO datasets. It achieves the following results on the evaluation set:

  • Loss: 0.6446
  • Rewards/chosen: -8.0278
  • Rewards/rejected: -9.5352
  • Rewards/accuracies: 0.7152
  • Rewards/margins: 1.5074
  • Logps/rejected: -1123.8456
  • Logps/chosen: -965.5345
  • Logits/rejected: 3.5622
  • Logits/chosen: 4.0391

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: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • total_eval_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • 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
0.6915 0.06 100 0.6917 -0.0059 -0.0087 0.5919 0.0028 -171.1956 -163.3472 -2.5867 -2.5707
0.6667 0.12 200 0.6690 -0.2049 -0.2607 0.6307 0.0558 -196.4011 -183.2503 -2.5361 -2.5294
0.6064 0.17 300 0.6131 -1.0874 -1.4208 0.6530 0.3333 -312.4040 -271.4992 -2.3765 -2.3824
0.5768 0.23 400 0.5798 -2.0019 -2.5132 0.7118 0.5113 -421.6484 -362.9495 -2.2241 -2.2088
0.5653 0.29 500 0.5732 -2.2365 -2.8068 0.7038 0.5703 -451.0063 -386.4047 -1.8327 -1.8721
0.5717 0.35 600 0.5686 -2.0292 -2.5806 0.7175 0.5514 -428.3890 -365.6780 -1.8751 -1.9234
0.5752 0.4 700 0.5646 -2.0035 -2.5598 0.7152 0.5563 -426.3091 -363.1083 -1.7231 -1.7178
0.5592 0.46 800 0.5595 -2.1767 -2.7903 0.7152 0.6135 -449.3554 -380.4316 -0.4741 -0.4635
0.5477 0.52 900 0.5613 -2.1853 -2.7708 0.7243 0.5854 -447.4023 -381.2917 -1.8590 -1.9478
0.5136 0.58 1000 0.5533 -2.1797 -2.8703 0.7226 0.6906 -457.3545 -380.7242 -1.6491 -1.7174
0.5555 0.63 1100 0.5573 -1.6655 -2.2517 0.7158 0.5862 -395.4941 -329.3049 -1.5555 -1.5565
0.5044 0.69 1200 0.5457 -2.5919 -3.3662 0.7203 0.7743 -506.9478 -421.9445 0.4933 0.5009
0.5078 0.75 1300 0.5505 -2.3710 -3.0599 0.7220 0.6889 -476.3146 -399.8520 0.4823 0.6094
0.5333 0.81 1400 0.5486 -2.3628 -3.0508 0.7175 0.6880 -475.4082 -399.0350 0.5794 0.6967
0.4799 0.86 1500 0.5452 -2.7663 -3.5674 0.7380 0.8011 -527.0656 -439.3846 1.2406 1.3814
0.5551 0.92 1600 0.5455 -2.6894 -3.4539 0.7329 0.7645 -515.7155 -431.6923 0.7892 0.8498
0.4911 0.98 1700 0.5509 -3.3307 -4.1684 0.7300 0.8376 -587.1636 -495.8297 2.3144 2.2622
0.3058 1.04 1800 0.5704 -4.5768 -5.6386 0.7215 1.0618 -734.1904 -620.4401 2.5171 2.4413
0.3346 1.09 1900 0.5765 -4.5531 -5.5699 0.7152 1.0168 -727.3204 -618.0657 2.0386 1.9196
0.3186 1.15 2000 0.5844 -5.1617 -6.2422 0.7140 1.0806 -794.5490 -678.9232 1.8747 1.7608
0.3032 1.21 2100 0.5746 -4.5098 -5.5583 0.7255 1.0485 -726.1542 -613.7318 1.8097 1.9375
0.3192 1.27 2200 0.5697 -4.5579 -5.6208 0.7215 1.0629 -732.4099 -618.5480 1.4935 1.6381
0.3047 1.32 2300 0.5830 -5.3394 -6.5272 0.7266 1.1877 -823.0447 -696.7006 1.9596 2.0880
0.3109 1.38 2400 0.5797 -4.8875 -6.0347 0.7192 1.1472 -773.7961 -651.5051 2.0438 2.2156
0.3165 1.44 2500 0.5704 -4.8449 -5.9117 0.7283 1.0668 -761.4922 -647.2463 1.6852 1.9232
0.321 1.5 2600 0.5705 -4.4244 -5.3853 0.7197 0.9609 -708.8524 -605.1918 1.8092 2.0444
0.3164 1.55 2700 0.5779 -5.0938 -6.1851 0.7169 1.0913 -788.8352 -672.1396 2.3926 2.6931
0.3201 1.61 2800 0.5634 -4.3216 -5.3414 0.7249 1.0197 -704.4624 -594.9215 1.9326 2.1325
0.3367 1.67 2900 0.5631 -4.6112 -5.6238 0.7255 1.0126 -732.7039 -623.8734 1.4794 1.6802
0.3414 1.73 3000 0.5698 -4.6100 -5.6200 0.7289 1.0100 -732.3315 -623.7572 1.6920 1.9589
0.3097 1.79 3100 0.5739 -4.9875 -6.1217 0.7255 1.1342 -782.4933 -661.5057 2.0260 2.2980
0.3077 1.84 3200 0.5685 -5.0298 -6.1319 0.7226 1.1021 -783.5215 -665.7410 2.0798 2.3995
0.3101 1.9 3300 0.5709 -5.0035 -6.1378 0.7352 1.1343 -784.1074 -663.1116 1.9782 2.2950
0.3235 1.96 3400 0.5629 -4.8491 -5.8527 0.7346 1.0035 -755.5942 -647.6710 1.9155 2.2626
0.1328 2.02 3500 0.6063 -6.6142 -7.9563 0.7289 1.3421 -965.9568 -824.1730 2.7098 3.0637
0.1438 2.07 3600 0.6421 -7.9002 -9.3674 0.7158 1.4671 -1107.0624 -952.7795 3.3994 3.8343
0.1474 2.13 3700 0.6611 -7.9802 -9.5452 0.7083 1.5651 -1124.8511 -960.7725 3.4598 3.9152
0.1267 2.19 3800 0.6578 -8.3961 -9.8684 0.7072 1.4723 -1157.1674 -1002.3674 3.7728 4.2505
0.117 2.25 3900 0.6595 -8.8743 -10.4271 0.7072 1.5528 -1213.0370 -1050.1907 3.8392 4.3287
0.1347 2.3 4000 0.6543 -8.3484 -9.8783 0.7049 1.5300 -1158.1610 -997.5932 3.6606 4.1056
0.1329 2.36 4100 0.6601 -8.2633 -9.8163 0.7158 1.5530 -1151.9531 -989.0843 3.4748 3.9028
0.1272 2.42 4200 0.6521 -8.3826 -9.9282 0.7129 1.5456 -1163.1472 -1001.0134 3.5794 4.0564
0.1398 2.48 4300 0.6440 -8.1928 -9.6983 0.7146 1.5054 -1140.1526 -982.0401 3.5277 4.0106
0.1452 2.53 4400 0.6379 -7.7709 -9.2597 0.7140 1.4888 -1096.2968 -939.8471 3.3970 3.8629
0.1686 2.59 4500 0.6465 -8.0350 -9.5456 0.7152 1.5106 -1124.8850 -966.2559 3.5100 3.9841
0.1626 2.65 4600 0.6461 -8.0584 -9.5877 0.7152 1.5293 -1129.0981 -968.5971 3.5312 4.0077
0.1496 2.71 4700 0.6474 -7.9977 -9.5321 0.7163 1.5344 -1123.5376 -962.5296 3.5337 4.0036
0.1418 2.76 4800 0.6431 -7.9795 -9.4898 0.7146 1.5103 -1119.3051 -960.7057 3.5538 4.0293
0.1505 2.82 4900 0.6432 -8.0170 -9.5172 0.7158 1.5002 -1122.0504 -964.4604 3.5728 4.0513
0.1321 2.88 5000 0.6443 -8.0235 -9.5310 0.7123 1.5075 -1123.4263 -965.1030 3.5611 4.0373
0.1269 2.94 5100 0.6447 -8.0373 -9.5449 0.7140 1.5076 -1124.8213 -966.4896 3.5691 4.0472
0.1417 2.99 5200 0.6446 -8.0277 -9.5354 0.7163 1.5078 -1123.8704 -965.5221 3.5627 4.0395

Framework versions

  • Transformers 4.39.0.dev0
  • Pytorch 2.3.0+cu121
  • Datasets 2.14.6
  • Tokenizers 0.15.2
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