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metadata
license: mit
library_name: peft
tags:
  - alignment-handbook
  - generated_from_trainer
  - trl
  - dpo
base_model: microsoft/phi-2
datasets:
  - HuggingFaceH4/ultrafeedback_binarized
model-index:
  - name: phi-2-gpo-renew2-b0.001-i1
    results: []

phi-2-gpo-renew2-b0.001-i1

This model is a fine-tuned version of DUAL-GPO/phi-2-gpo-renew2-b0.001-i0 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0538
  • Rewards/chosen: 0.0010
  • Rewards/rejected: 0.0012
  • Rewards/accuracies: 0.4290
  • Rewards/margins: -0.0002
  • Logps/rejected: -366.0280
  • Logps/chosen: -395.2844
  • Logits/rejected: -0.7463
  • Logits/chosen: -0.8436

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: 4
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 4
  • total_train_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 Logits/chosen Logits/rejected Logps/chosen Logps/rejected Validation Loss Rewards/accuracies Rewards/chosen Rewards/margins Rewards/rejected
0.1204 0.32 100 -0.8372 -0.7448 -396.1279 -367.0282 0.0537 0.4495 0.0002 -0.0000 0.0002
0.1673 0.64 200 0.0538 0.0013 0.0015 0.4305 -0.0002 -365.7495 -395.0410 -0.7569 -0.8518
0.1395 0.96 300 0.0538 0.0010 0.0012 0.4395 -0.0002 -365.9886 -395.3006 -0.7587 -0.8541

Framework versions

  • PEFT 0.7.1
  • Transformers 4.36.2
  • Pytorch 2.1.2
  • Datasets 2.14.6
  • Tokenizers 0.15.2