ft-openelm-270m-ultrafeedback

This model is a fine-tuned version of apple/OpenELM-270M on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

  • Loss: 1.6455
  • Rewards/chosen: -0.1993
  • Rewards/rejected: -0.2029
  • Rewards/accuracies: 0.5050
  • Rewards/margins: 0.0035
  • Logps/rejected: -2.0273
  • Logps/chosen: -1.9941
  • Logits/rejected: -5.7383
  • Logits/chosen: -6.1094
  • Nll Loss: 1.5742
  • Log Odds Ratio: -0.7037
  • Log Odds Chosen: 0.0445

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: 8e-06
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • 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 Nll Loss Log Odds Ratio Log Odds Chosen
1.7594 0.53 100 1.6455 -0.1993 -0.2029 0.5050 0.0035 -2.0273 -1.9941 -5.7383 -6.1094 1.5742 -0.7037 0.0445

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.4.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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