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idefics2_ft_augmented_dataset

This model is a fine-tuned version of HuggingFaceM4/idefics2-8b on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1871

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: 2.5e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 25
  • training_steps: 5000

Training results

Training Loss Epoch Step Validation Loss
0.6338 0.0234 100 0.6474
0.5748 0.0468 200 0.5899
0.4678 0.0702 300 0.5603
0.428 0.0936 400 0.5249
0.3798 0.1170 500 0.4984
0.3665 0.1404 600 0.4733
0.4406 0.1637 700 0.4510
0.4723 0.1871 800 0.4245
0.4807 0.2105 900 0.4158
0.4196 0.2339 1000 0.3971
0.3443 0.2573 1100 0.3738
0.4133 0.2807 1200 0.3631
0.2838 0.3041 1300 0.3334
0.4134 0.3275 1400 0.3264
0.2838 0.3509 1500 0.3125
0.275 0.3743 1600 0.2944
0.4141 0.3977 1700 0.2839
0.2498 0.4211 1800 0.2749
0.2817 0.4444 1900 0.2606
0.2899 0.4678 2000 0.2526
0.2695 0.4912 2100 0.2521
0.2619 0.5146 2200 0.2424
0.2238 0.5380 2300 0.2373
0.3049 0.5614 2400 0.2301
0.1308 0.5848 2500 0.2292
0.1936 0.6082 2600 0.2190
0.2479 0.6316 2700 0.2191
0.1575 0.6550 2800 0.2165
0.193 0.6784 2900 0.2107
0.2526 0.7018 3000 0.2114
0.1574 0.7251 3100 0.2087
0.1989 0.7485 3200 0.2051
0.1761 0.7719 3300 0.2013
0.2223 0.7953 3400 0.1996
0.2127 0.8187 3500 0.1966
0.2477 0.8421 3600 0.1923
0.1931 0.8655 3700 0.1908
0.182 0.8889 3800 0.1888
0.1693 0.9123 3900 0.1878
0.1346 0.9357 4000 0.1853
0.1484 0.9591 4100 0.1849
0.1217 0.9825 4200 0.1838
0.0669 1.0058 4300 0.1844
0.1292 1.0292 4400 0.1877
0.1106 1.0526 4500 0.1876
0.0828 1.0760 4600 0.1875
0.0485 1.0994 4700 0.1871
0.0624 1.1228 4800 0.1874
0.0895 1.1462 4900 0.1871
0.1 1.1696 5000 0.1871

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1
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