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metadata
license: other
base_model: lewtun/gemma-7b-sft-full-deita-10k-v0
tags:
  - alignment-handbook
  - trl
  - dpo
  - generated_from_trainer
  - trl
  - dpo
  - generated_from_trainer
datasets:
  - HuggingFaceH4/ultrafeedback_binarized
  - HuggingFaceH4/orca_dpo_pairs
model-index:
  - name: gemma-7b-dpo-full-mix2-beta-0.1
    results: []

gemma-7b-dpo-full-mix2-beta-0.1

This model is a fine-tuned version of lewtun/gemma-7b-sft-full-deita-10k-v0 on the HuggingFaceH4/ultrafeedback_binarized and the HuggingFaceH4/orca_dpo_pairs datasets. It achieves the following results on the evaluation set:

  • Loss: 0.4056
  • Rewards/chosen: -0.3995
  • Rewards/rejected: -3.5721
  • Rewards/accuracies: 0.7926
  • Rewards/margins: 3.1726
  • Logps/rejected: -414.5198
  • Logps/chosen: -392.3416
  • Logits/rejected: 83.8425
  • Logits/chosen: 83.1641

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
0.6146 0.18 100 0.4651 -2.3366 -4.2121 0.7527 1.8755 -420.9197 -411.7124 84.3991 81.8795
0.5464 0.35 200 0.4531 -0.7850 -3.1857 0.7899 2.4007 -410.6562 -396.1968 84.8764 82.9057
0.5841 0.53 300 0.4209 -1.5926 -4.2403 0.8085 2.6477 -421.2023 -404.2725 83.7612 81.9224
0.519 0.7 400 0.4162 -1.2384 -4.1774 0.7819 2.9390 -420.5732 -400.7308 85.8201 84.7816
0.5432 0.88 500 0.4134 -0.3763 -3.5060 0.8032 3.1296 -413.8586 -392.1099 83.6363 82.8991

Framework versions

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