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sablo-pebble-mistral-dpo-lora-HelpSteer_binarized

This model is a fine-tuned version of sablo/sablo-pebble-mistral on the sablo/HelpSteer_binarized dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5371
  • Rewards/chosen: -0.9335
  • Rewards/rejected: -1.6455
  • Rewards/accuracies: 0.7264
  • Rewards/margins: 0.7121
  • Logps/rejected: -298.0735
  • Logps/chosen: -253.4149
  • Logits/rejected: -2.4554
  • Logits/chosen: -2.5093

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: 8
  • seed: 42
  • distributed_type: multi-GPU
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • 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 Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.6874 0.1 100 0.6892 0.0213 0.0133 0.6698 0.0080 -132.1924 -157.9395 -2.4463 -2.4843
0.6592 0.2 200 0.6594 0.0055 -0.0704 0.6698 0.0759 -140.5588 -159.5180 -2.4922 -2.5370
0.5451 0.3 300 0.5867 -0.4490 -0.7587 0.6863 0.3097 -209.3938 -204.9713 -2.5128 -2.5620
0.4933 0.39 400 0.5591 -0.6060 -1.1029 0.7146 0.4968 -243.8062 -220.6713 -2.4868 -2.5386
0.5271 0.49 500 0.5488 -0.6712 -1.2738 0.7193 0.6026 -260.8958 -227.1889 -2.4784 -2.5312
0.4594 0.59 600 0.5418 -0.7977 -1.4672 0.7311 0.6695 -280.2420 -239.8430 -2.4672 -2.5200
0.5444 0.69 700 0.5358 -0.7688 -1.4528 0.7335 0.6840 -278.8014 -236.9531 -2.4594 -2.5127
0.5755 0.79 800 0.5405 -1.0672 -1.7631 0.7311 0.6959 -309.8293 -266.7906 -2.4585 -2.5118
0.5495 0.89 900 0.5371 -0.9321 -1.6450 0.7288 0.7129 -298.0242 -253.2804 -2.4558 -2.5096
0.5948 0.98 1000 0.5371 -0.9335 -1.6455 0.7264 0.7121 -298.0735 -253.4149 -2.4554 -2.5093

Framework versions

  • PEFT 0.7.1
  • Transformers 4.36.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.6
  • Tokenizers 0.15.0
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