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metadata
library_name: transformers
license: apache-2.0
base_model: alignment-handbook/zephyr-7b-sft-full
tags:
  - alignment-handbook
  - trl
  - dpo
  - generated_from_trainer
  - trl
  - dpo
  - generated_from_trainer
datasets:
  - HuggingFaceH4/ultrafeedback_binarized
model-index:
  - name: zephyr-7b-group-dpo-full-4
    results: []

zephyr-7b-group-dpo-full-4

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: 0.3329
  • Rewards/chosen: -3.5718
  • Rewards/rejected: -6.6220
  • Rewards/accuracies: 0.8281
  • Rewards/margins: 3.0502
  • Logps/rejected: -952.0885
  • Logps/chosen: -639.7113
  • Logits/rejected: -0.6186
  • Logits/chosen: -1.1189

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: 8
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 256
  • 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: 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
0.2094 0.2303 100 0.4273 -1.2973 -2.4710 0.7768 1.1737 -536.9892 -412.2621 -2.8747 -2.8886
0.1232 0.4606 200 0.3541 -2.8531 -5.1625 0.8069 2.3094 -806.1392 -567.8411 -1.2949 -1.6181
0.1 0.6908 300 0.3383 -3.9067 -6.9476 0.8158 3.0409 -984.6500 -673.2026 -0.4879 -0.9150
0.0956 0.9211 400 0.3329 -3.5971 -6.6557 0.8281 3.0586 -955.4623 -642.2396 -0.6052 -1.1035

Framework versions

  • Transformers 4.44.1
  • Pytorch 2.1.2+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1