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metadata
base_model: alignment-handbook/zephyr-7b-sft-full
library_name: peft
license: apache-2.0
tags:
  - trl
  - dpo
  - generated_from_trainer
model-index:
  - name: zephyr-dpo-qlora-uf-ours-5e-6-epoch1
    results: []

zephyr-dpo-qlora-uf-ours-5e-6-epoch1

This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8935
  • Rewards/chosen: -4.8886
  • Rewards/rejected: -5.8224
  • Rewards/accuracies: 0.6470
  • Rewards/margins: 0.9339
  • Rewards/margins Max: 4.5089
  • Rewards/margins Min: -2.8033
  • Rewards/margins Std: 2.4986
  • Logps/rejected: -840.8231
  • Logps/chosen: -773.4503
  • Logits/rejected: -1.3417
  • Logits/chosen: -1.4023

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-06
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • total_eval_batch_size: 16
  • 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 Rewards/margins Max Rewards/margins Min Rewards/margins Std Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.4419 0.28 100 0.6650 -0.4529 -0.5977 0.6240 0.1449 0.8619 -0.5418 0.4692 -318.3518 -329.8811 -2.5449 -2.5719
0.1872 0.56 200 0.7859 -2.8392 -3.5400 0.6630 0.7008 3.4374 -2.1827 1.9158 -612.5828 -568.5178 -1.4159 -1.4771
0.1102 0.85 300 0.8935 -4.8886 -5.8224 0.6470 0.9339 4.5089 -2.8033 2.4986 -840.8231 -773.4503 -1.3417 -1.4023

Framework versions

  • PEFT 0.7.1
  • Transformers 4.39.0.dev0
  • Pytorch 2.1.2+cu121
  • Datasets 2.14.6
  • Tokenizers 0.15.2