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zephyr-7b-dpo-lora

This model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5082
  • Rewards/chosen: 0.0025
  • Rewards/rejected: -0.9047
  • Rewards/accuracies: 0.7222
  • Rewards/margins: 0.9072
  • Logps/rejected: -276.6852
  • Logps/chosen: -271.8461
  • Logits/rejected: -2.7167
  • Logits/chosen: -2.7365

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
  • num_devices: 4
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • total_eval_batch_size: 32
  • 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
0.5795 0.1047 100 0.5875 0.0265 -0.3721 0.6825 0.3986 -271.3593 -271.6063 -2.7688 -2.7900
0.5449 0.2094 200 0.5520 0.0601 -0.5726 0.7103 0.6327 -273.3645 -271.2704 -2.7792 -2.7981
0.545 0.3141 300 0.5320 -0.0197 -0.7637 0.7044 0.7439 -275.2751 -272.0686 -2.7616 -2.7803
0.4747 0.4187 400 0.5228 -0.1728 -0.9527 0.7004 0.7798 -277.1651 -273.5996 -2.7532 -2.7732
0.5367 0.5234 500 0.5175 -0.2142 -1.0435 0.7143 0.8293 -278.0737 -274.0135 -2.7339 -2.7540
0.5031 0.6281 600 0.5139 -0.2939 -1.1329 0.7024 0.8389 -278.9670 -274.8105 -2.7071 -2.7268
0.5057 0.7328 700 0.5084 -0.0108 -0.9049 0.7202 0.8941 -276.6876 -271.9794 -2.7207 -2.7404
0.5172 0.8375 800 0.5090 -0.0300 -0.9231 0.7183 0.8931 -276.8697 -272.1711 -2.7173 -2.7371
0.5173 0.9422 900 0.5084 -0.0008 -0.9080 0.7222 0.9072 -276.7181 -271.8789 -2.7174 -2.7372

Framework versions

  • PEFT 0.7.1
  • Transformers 4.45.2
  • Pytorch 2.4.0+cu121
  • Datasets 2.14.6
  • Tokenizers 0.20.1
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