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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: 0.4963
  • Rewards/chosen: -1.3619
  • Rewards/rejected: -2.3893
  • Rewards/accuracies: 0.7619
  • Rewards/margins: 1.0274
  • Logps/rejected: -499.1414
  • Logps/chosen: -418.1593
  • Logits/rejected: 1.1001
  • Logits/chosen: 0.2372

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: 1

Training results

Training Loss Epoch Step Logits/chosen Logits/rejected Logps/chosen Logps/rejected Validation Loss Rewards/accuracies Rewards/chosen Rewards/margins Rewards/rejected
0.6267 0.1047 100 -2.4885 -2.4228 -298.8784 -298.6605 0.6219 0.7123 -0.1691 0.2154 -0.3845
0.5618 0.2093 200 -1.1643 -0.9025 -368.7673 -409.5043 0.5559 0.75 -0.8680 0.6250 -1.4930
0.5298 0.3140 300 0.1099 0.5848 -387.0007 -442.8027 0.5283 0.7679 -1.0503 0.7756 -1.8260
0.5585 0.4186 400 -1.2260 -0.6461 -368.9012 -419.9413 0.5200 0.7639 -0.8693 0.7280 -1.5973
0.5074 0.5233 500 0.1651 0.9655 -402.5899 -472.5687 0.5043 0.7698 -1.2062 0.9174 -2.1236
0.4678 0.6279 600 -0.1276 0.7528 -405.3750 -480.0774 0.4995 0.7698 -1.2341 0.9646 -2.1987
0.4767 0.7326 700 0.4974 -1.3438 -2.3581 0.7619 1.0142 -496.0146 -416.3518 1.1957 0.3567
0.475 0.8373 800 0.4971 -1.3985 -2.3889 0.7639 0.9904 -499.1005 -421.8184 1.0101 0.2102
0.4828 0.9419 900 0.4963 -1.3653 -2.3944 0.7639 1.0292 -499.6516 -418.4965 1.1202 0.2514

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1
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