zephyr-7b-dpo-full / README.md
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metadata
tags:
  - trl
  - dpo
  - generated_from_trainer
model-index:
  - name: zephyr-7b-dpo-full
    results: []

zephyr-7b-dpo-full

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0224
  • Rewards/chosen: -1.9945
  • Rewards/rejected: -3.2919
  • Rewards/accuracies: 0.7148
  • Rewards/margins: 1.2974
  • Logps/rejected: -640.8138
  • Logps/chosen: -503.0325
  • Logits/rejected: 0.3215
  • Logits/chosen: 0.2841

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-06
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 4
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • total_eval_batch_size: 64
  • 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
0.111 0.21 100 0.1080 -0.3300 -0.6434 0.7148 0.3134 -375.9606 -336.5851 0.4520 0.3976
0.0697 0.42 200 0.0728 -0.5844 -1.2213 0.7422 0.6369 -433.7567 -362.0242 0.4101 0.3267
0.055 0.63 300 0.0610 -0.7945 -1.5421 0.7266 0.7476 -465.8376 -383.0369 0.2780 0.2451
0.0573 0.84 400 0.0566 -0.8305 -1.5952 0.7383 0.7647 -471.1477 -386.6394 0.2561 0.2348
0.0215 1.05 500 0.0327 -1.6150 -2.8668 0.7305 1.2517 -598.3008 -465.0880 0.2419 0.2221
0.0139 1.26 600 0.0260 -1.8080 -3.0895 0.7227 1.2815 -620.5768 -484.3871 0.2916 0.2601
0.0125 1.47 700 0.0247 -1.9121 -3.1886 0.7305 1.2765 -630.4850 -494.7950 0.2947 0.2614
0.0107 1.67 800 0.0226 -1.9947 -3.2951 0.7188 1.3004 -641.1344 -503.0576 0.3196 0.2841
0.0106 1.88 900 0.0224 -1.9945 -3.2919 0.7148 1.2974 -640.8138 -503.0325 0.3215 0.2841

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.14.6
  • Tokenizers 0.14.1