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metadata
library_name: peft
tags:
  - trl
  - dpo
  - DPO
  - WeniGPT
  - generated_from_trainer
base_model: Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged
model-index:
  - name: WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.8-DPO
    results: []

WeniGPT-Agents-Mistral-1.0.6-SFT-1.0.8-DPO

This model is a fine-tuned version of Weni/WeniGPT-Agents-Mistral-1.0.6-SFT-merged on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2005
  • Rewards/chosen: 1.7621
  • Rewards/rejected: -0.7248
  • Rewards/accuracies: 1.0
  • Rewards/margins: 2.4869
  • Logps/rejected: -271.5133
  • Logps/chosen: -102.9727
  • Logits/rejected: -1.8958
  • Logits/chosen: -1.8013

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: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • total_eval_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.03
  • training_steps: 180
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.5477 0.97 30 0.4843 0.4718 -0.1109 0.8571 0.5827 -269.4668 -107.2735 -1.8863 -1.7949
0.3542 1.94 60 0.3440 0.9609 -0.2516 1.0 1.2125 -269.9360 -105.6431 -1.8903 -1.7979
0.2892 2.9 90 0.2756 1.3199 -0.4182 1.0 1.7381 -270.4914 -104.4467 -1.8928 -1.7995
0.1858 3.87 120 0.2327 1.6010 -0.5696 1.0 2.1706 -270.9958 -103.5094 -1.8947 -1.8008
0.1811 4.84 150 0.2076 1.7245 -0.6925 1.0 2.4170 -271.4054 -103.0979 -1.8954 -1.8010
0.2065 5.81 180 0.2005 1.7621 -0.7248 1.0 2.4869 -271.5133 -102.9727 -1.8958 -1.8013

Framework versions

  • PEFT 0.10.0
  • Transformers 4.38.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.18.0
  • Tokenizers 0.15.2