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metadata
license: other
base_model: lewtun/gemma-7b-sft-full-ultrachat-v0
tags:
  - alignment-handbook
  - trl
  - dpo
  - generated_from_trainer
  - trl
  - dpo
  - generated_from_trainer
datasets:
  - HuggingFaceH4/ultrafeedback_binarized
model-index:
  - name: gemma-7b-dpo-full-ultrafeedback-beta-0.01
    results: []

gemma-7b-dpo-full-ultrafeedback-beta-0.01

This model is a fine-tuned version of lewtun/gemma-7b-sft-full-ultrachat-v0 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4698
  • Rewards/chosen: -1.0027
  • Rewards/rejected: -2.3339
  • Rewards/accuracies: 0.7698
  • Rewards/margins: 1.3312
  • Logps/rejected: -1118.8601
  • Logps/chosen: -1006.0907
  • Logits/rejected: 90.6424
  • Logits/chosen: 105.6680

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: 2
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 128
  • 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 Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.552 0.21 100 0.5756 -2.8657 -3.5901 0.7460 0.7243 -1244.4771 -1192.3933 82.3244 96.5612
0.501 0.42 200 0.4914 -1.6427 -2.6660 0.7817 1.0233 -1152.0745 -1070.0895 91.1202 105.1467
0.4893 0.63 300 0.4810 -1.6604 -2.8398 0.7619 1.1794 -1169.4480 -1071.8550 87.4237 101.9799
0.4759 0.84 400 0.4718 -0.8508 -2.1538 0.7817 1.3030 -1100.8470 -990.8950 89.1600 104.0108

Framework versions

  • Transformers 4.39.0.dev0
  • Pytorch 2.1.2+cu121
  • Datasets 2.14.6
  • Tokenizers 0.15.1