bbfc8e75dac0d6ea1ca69ac37ff5beaa

This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B on the nyu-mll/glue dataset. It achieves the following results on the evaluation set:

  • Loss: 5.0891
  • Data Size: 1.0
  • Epoch Runtime: 33.8007
  • Accuracy: 0.8149
  • F1 Macro: 0.7728
  • Rouge1: 0.8149
  • Rouge2: 0.0
  • Rougel: 0.8149
  • Rougelsum: 0.8154

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-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • total_train_batch_size: 32
  • total_eval_batch_size: 32
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: constant
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Data Size Epoch Runtime Accuracy F1 Macro Rouge1 Rouge2 Rougel Rougelsum
No log 0 0 7.0905 0 4.4920 0.3544 0.2989 0.3538 0.0 0.3538 0.3544
No log 1 114 7.6449 0.0078 4.3656 0.6639 0.4121 0.6645 0.0 0.6639 0.6639
No log 2 228 3.1042 0.0156 7.0459 0.5731 0.5525 0.5725 0.0 0.5728 0.5731
No log 3 342 2.7720 0.0312 10.1902 0.6769 0.5282 0.6769 0.0 0.6763 0.6763
0.1341 4 456 2.3659 0.0625 12.0846 0.6881 0.6353 0.6881 0.0 0.6881 0.6881
0.1341 5 570 2.1620 0.125 14.6870 0.7488 0.6646 0.7482 0.0 0.7488 0.7494
0.1341 6 684 1.7724 0.25 19.3808 0.8013 0.7475 0.8019 0.0 0.8019 0.8013
0.4926 7 798 1.5741 0.5 21.4771 0.8261 0.8001 0.8255 0.0 0.8261 0.8261
0.9023 8.0 912 1.7902 1.0 32.0793 0.8090 0.7649 0.8093 0.0 0.8090 0.8096
0.5336 9.0 1026 3.7415 1.0 32.3208 0.7995 0.7667 0.7989 0.0 0.8001 0.7995
0.4963 10.0 1140 4.2174 1.0 31.7095 0.8160 0.7719 0.8160 0.0 0.8166 0.8166
0.1717 11.0 1254 5.0891 1.0 33.8007 0.8149 0.7728 0.8149 0.0 0.8149 0.8154

Framework versions

  • Transformers 4.57.0
  • Pytorch 2.8.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.1
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