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metadata
base_model: alignment-handbook/zephyr-7b-sft-full
datasets:
  - generation/UF6konly
library_name: peft
license: apache-2.0
tags:
  - alignment-handbook
  - trl
  - dpo
  - generated_from_trainer
model-index:
  - name: zephyr-dpo-qlora-uf6k-5e-7
    results: []

zephyr-dpo-qlora-uf6k-5e-7

This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the generation/UF6konly dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6890
  • Rewards/chosen: 0.0030
  • Rewards/rejected: -0.0067
  • Rewards/accuracies: 0.6750
  • Rewards/margins: 0.0097
  • Rewards/margins Max: 0.0448
  • Rewards/margins Min: -0.0217
  • Rewards/margins Std: 0.0219
  • Logps/rejected: -259.2527
  • Logps/chosen: -284.2960
  • Logits/rejected: -2.7684
  • Logits/chosen: -2.8066

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: 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
0.6914 0.3 100 0.6915 -0.0004 -0.0041 0.6460 0.0037 0.0187 -0.0095 0.0093 -258.9901 -284.6319 -2.7677 -2.8065
0.6884 0.61 200 0.6895 0.0023 -0.0061 0.6850 0.0084 0.0389 -0.0189 0.0190 -259.1880 -284.3611 -2.7665 -2.8049
0.6873 0.91 300 0.6889 0.0031 -0.0066 0.6760 0.0098 0.0449 -0.0216 0.0219 -259.2438 -284.2815 -2.7640 -2.8026

Framework versions

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