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zephyr-7b-dpo-full

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

  • Loss: 0.5590
  • Rewards/chosen: -0.7818
  • Rewards/rejected: -2.7115
  • Rewards/accuracies: 0.7857
  • Rewards/margins: 1.9297
  • Logps/rejected: -287.3273
  • Logps/chosen: -289.7805
  • Logits/rejected: -2.4561
  • Logits/chosen: -2.5007

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: 2

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.6075 0.1 100 0.5945 0.3241 -0.1206 0.7163 0.4447 -261.4175 -278.7209 -2.6324 -2.6651
0.5341 0.21 200 0.5471 -0.0734 -1.0103 0.7639 0.9369 -270.3152 -282.6963 -2.5394 -2.5779
0.5315 0.31 300 0.5258 0.1435 -0.9757 0.7619 1.1192 -269.9694 -280.5274 -2.5337 -2.5711
0.4978 0.42 400 0.5366 -0.2177 -1.2826 0.7579 1.0649 -273.0383 -284.1391 -2.5667 -2.6011
0.5134 0.52 500 0.5340 -0.4713 -1.5140 0.7460 1.0427 -275.3516 -286.6748 -2.4488 -2.4836
0.5404 0.63 600 0.5188 -0.0534 -1.2981 0.7480 1.2447 -273.1928 -282.4962 -2.3631 -2.4039
0.5256 0.73 700 0.5270 -0.2533 -1.5704 0.7639 1.3172 -275.9163 -284.4948 -2.3224 -2.3640
0.4991 0.84 800 0.5278 -0.2394 -1.5276 0.7639 1.2882 -275.4879 -284.3556 -2.3730 -2.4144
0.5084 0.94 900 0.5457 0.2664 -0.9546 0.7619 1.2210 -269.7581 -279.2981 -2.4875 -2.5254
0.1011 1.05 1000 0.5361 -0.5236 -2.1364 0.7877 1.6129 -281.5762 -287.1976 -2.4389 -2.4774
0.0942 1.15 1100 0.5454 -0.4356 -2.2047 0.7897 1.7691 -282.2592 -286.3182 -2.4515 -2.4926
0.0817 1.26 1200 0.5530 -0.7588 -2.5855 0.7857 1.8268 -286.0674 -289.5495 -2.4441 -2.4863
0.0697 1.36 1300 0.5549 -0.5919 -2.4690 0.7798 1.8771 -284.9021 -287.8810 -2.4474 -2.4910
0.0842 1.47 1400 0.5575 -0.7425 -2.6443 0.7917 1.9018 -286.6550 -289.3871 -2.4669 -2.5100
0.075 1.57 1500 0.5590 -0.5382 -2.4532 0.7956 1.9150 -284.7438 -287.3436 -2.4699 -2.5133
0.098 1.67 1600 0.5583 -0.7761 -2.6741 0.7877 1.8980 -286.9528 -289.7227 -2.4652 -2.5092
0.0718 1.78 1700 0.5593 -0.7532 -2.6704 0.7877 1.9172 -286.9160 -289.4940 -2.4592 -2.5036
0.0828 1.88 1800 0.5606 -0.7985 -2.7306 0.7897 1.9321 -287.5178 -289.9467 -2.4560 -2.5007
0.103 1.99 1900 0.5601 -0.7805 -2.7113 0.7857 1.9309 -287.3255 -289.7666 -2.4554 -2.5002

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2
  • Datasets 2.14.6
  • Tokenizers 0.15.2
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