tinyllama_mole_dpo_ep3

This model is a fine-tuned version of ondevicellm/tinyllama_mole_sft_ultrachat_ep3 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6285
  • Rewards/chosen: -0.3050
  • Rewards/rejected: -0.5353
  • Rewards/accuracies: 0.6806
  • Rewards/margins: 0.2302
  • Logps/rejected: -354.2071
  • Logps/chosen: -373.1399
  • Logits/rejected: -1.6731
  • Logits/chosen: -1.8041

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-07
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • 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_steps: 100
  • 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
0.6896 0.1 100 0.6899 0.0064 -0.0013 0.6448 0.0076 -300.8089 -342.0017 -1.7574 -1.8918
0.6762 0.21 200 0.6756 -0.0293 -0.0716 0.6627 0.0423 -307.8423 -345.5688 -1.7501 -1.8839
0.6499 0.31 300 0.6587 -0.0875 -0.1813 0.6687 0.0938 -318.8118 -351.3895 -1.7358 -1.8688
0.6374 0.42 400 0.6451 -0.1726 -0.3218 0.6746 0.1493 -332.8632 -359.8953 -1.7164 -1.8482
0.6348 0.52 500 0.6377 -0.2696 -0.4550 0.6647 0.1854 -346.1808 -369.6013 -1.6884 -1.8208
0.6308 0.63 600 0.6333 -0.2783 -0.4815 0.6726 0.2032 -348.8291 -370.4673 -1.6965 -1.8269
0.62 0.73 700 0.6312 -0.2323 -0.4505 0.6806 0.2182 -345.7306 -365.8656 -1.6841 -1.8149
0.6055 0.84 800 0.6287 -0.2877 -0.5169 0.6865 0.2292 -352.3697 -371.4099 -1.6793 -1.8099
0.6357 0.94 900 0.6285 -0.3050 -0.5353 0.6806 0.2302 -354.2071 -373.1399 -1.6731 -1.8041

Framework versions

  • Transformers 4.37.0
  • Pytorch 2.1.2+cu118
  • Datasets 2.16.1
  • Tokenizers 0.15.0
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