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metadata
library_name: transformers
license: apache-2.0
base_model: tsavage68/IE_M2_1000steps_1e7rate_SFT
tags:
  - trl
  - dpo
  - generated_from_trainer
model-index:
  - name: IE_M2_350steps_1e8rate_03beta_cSFTDPO
    results: []

IE_M2_350steps_1e8rate_03beta_cSFTDPO

This model is a fine-tuned version of tsavage68/IE_M2_1000steps_1e7rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6746
  • Rewards/chosen: -0.0013
  • Rewards/rejected: -0.0404
  • Rewards/accuracies: 0.3600
  • Rewards/margins: 0.0391
  • Logps/rejected: -41.1564
  • Logps/chosen: -42.2098
  • Logits/rejected: -2.9159
  • Logits/chosen: -2.8545

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: 1e-08
  • train_batch_size: 2
  • eval_batch_size: 1
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 100
  • training_steps: 350

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.6998 0.4 50 0.6949 0.0058 0.0085 0.2050 -0.0028 -40.9934 -42.1863 -2.9160 -2.8547
0.6925 0.8 100 0.6906 0.0017 -0.0041 0.2600 0.0059 -41.0355 -42.1997 -2.9159 -2.8546
0.679 1.2 150 0.6779 0.0047 -0.0273 0.3650 0.0320 -41.1127 -42.1899 -2.9158 -2.8546
0.6715 1.6 200 0.6747 0.0020 -0.0367 0.3900 0.0387 -41.1442 -42.1988 -2.9156 -2.8544
0.6764 2.0 250 0.6736 -0.0012 -0.0419 0.3850 0.0407 -41.1614 -42.2094 -2.9156 -2.8543
0.6842 2.4 300 0.6763 -0.0024 -0.0380 0.3500 0.0355 -41.1483 -42.2137 -2.9159 -2.8545
0.6712 2.8 350 0.6746 -0.0013 -0.0404 0.3600 0.0391 -41.1564 -42.2098 -2.9159 -2.8545

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.0.0+cu117
  • Datasets 3.0.0
  • Tokenizers 0.19.1