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ARC-Easy_Llama-3.2-1B-u85m8hv7

Text Generation
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llama
Generated from Trainer
text-generation-inference
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xet
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ARC-Easy_Llama-3.2-1B-u85m8hv7

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 4.4189
  • Model Preparation Time: 0.006
  • Mdl: 3633.8424
  • Accumulated Loss: 2518.7876
  • Correct Preds: 377.0
  • Total Preds: 570.0
  • Accuracy: 0.6614
  • Correct Gen Preds: 377.0
  • Gen Accuracy: 0.6614
  • Correct Gen Preds 32: 108.0
  • Correct Preds 32: 108.0
  • Total Labels 32: 158.0
  • Accuracy 32: 0.6835
  • Gen Accuracy 32: 0.6835
  • Correct Gen Preds 33: 106.0
  • Correct Preds 33: 106.0
  • Total Labels 33: 152.0
  • Accuracy 33: 0.6974
  • Gen Accuracy 33: 0.6974
  • Correct Gen Preds 34: 91.0
  • Correct Preds 34: 91.0
  • Total Labels 34: 142.0
  • Accuracy 34: 0.6408
  • Gen Accuracy 34: 0.6408
  • Correct Gen Preds 35: 72.0
  • Correct Preds 35: 72.0
  • Total Labels 35: 118.0
  • Accuracy 35: 0.6102
  • Gen Accuracy 35: 0.6102
  • Correct Gen Preds 36: 0.0
  • Correct Preds 36: 0.0
  • Total Labels 36: 0.0
  • Accuracy 36: 0.0
  • Gen Accuracy 36: 0.0

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: 2e-05
  • train_batch_size: 64
  • eval_batch_size: 112
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.01
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Model Preparation Time Mdl Accumulated Loss Correct Preds Total Preds Accuracy Correct Gen Preds Gen Accuracy Correct Gen Preds 32 Correct Preds 32 Total Labels 32 Accuracy 32 Gen Accuracy 32 Correct Gen Preds 33 Correct Preds 33 Total Labels 33 Accuracy 33 Gen Accuracy 33 Correct Gen Preds 34 Correct Preds 34 Total Labels 34 Accuracy 34 Gen Accuracy 34 Correct Gen Preds 35 Correct Preds 35 Total Labels 35 Accuracy 35 Gen Accuracy 35 Correct Gen Preds 36 Correct Preds 36 Total Labels 36 Accuracy 36 Gen Accuracy 36
No log 0 0 1.5354 0.006 1262.6022 875.1692 172.0 570.0 0.3018 170.0 0.2982 154.0 154.0 158.0 0.9747 0.9747 0.0 0.0 152.0 0.0 0.0 15.0 17.0 142.0 0.1197 0.1056 1.0 1.0 118.0 0.0085 0.0085 0.0 0.0 0.0 0.0 0.0
1.4008 1.0 1 1.5354 0.006 1262.6022 875.1692 172.0 570.0 0.3018 170.0 0.2982 154.0 154.0 158.0 0.9747 0.9747 0.0 0.0 152.0 0.0 0.0 15.0 17.0 142.0 0.1197 0.1056 1.0 1.0 118.0 0.0085 0.0085 0.0 0.0 0.0 0.0 0.0
1.4008 2.0 2 2.1073 0.006 1732.9467 1201.1871 152.0 570.0 0.2667 152.0 0.2667 0.0 0.0 158.0 0.0 0.0 152.0 152.0 152.0 1.0 1.0 0.0 0.0 142.0 0.0 0.0 0.0 0.0 118.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.8703 3.0 3 1.4116 0.006 1160.7698 804.5843 221.0 570.0 0.3877 221.0 0.3877 128.0 128.0 158.0 0.8101 0.8101 13.0 13.0 152.0 0.0855 0.0855 0.0 0.0 142.0 0.0 0.0 80.0 80.0 118.0 0.6780 0.6780 0.0 0.0 0.0 0.0 0.0
0.8616 4.0 4 2.9120 0.006 2394.6752 1659.8624 229.0 570.0 0.4018 229.0 0.4018 156.0 156.0 158.0 0.9873 0.9873 9.0 9.0 152.0 0.0592 0.0592 36.0 36.0 142.0 0.2535 0.2535 28.0 28.0 118.0 0.2373 0.2373 0.0 0.0 0.0 0.0 0.0
0.8814 5.0 5 1.2337 0.006 1014.5261 703.2159 352.0 570.0 0.6175 351.0 0.6158 119.0 120.0 158.0 0.7595 0.7532 59.0 59.0 152.0 0.3882 0.3882 101.0 101.0 142.0 0.7113 0.7113 72.0 72.0 118.0 0.6102 0.6102 0.0 0.0 0.0 0.0 0.0
0.2693 6.0 6 1.3112 0.006 1078.2076 747.3565 359.0 570.0 0.6298 358.0 0.6281 58.0 59.0 158.0 0.3734 0.3671 112.0 112.0 152.0 0.7368 0.7368 110.0 110.0 142.0 0.7746 0.7746 78.0 78.0 118.0 0.6610 0.6610 0.0 0.0 0.0 0.0 0.0
0.1177 7.0 7 1.6180 0.006 1330.5221 922.2476 364.0 570.0 0.6386 363.0 0.6368 66.0 67.0 158.0 0.4241 0.4177 114.0 114.0 152.0 0.75 0.75 106.0 106.0 142.0 0.7465 0.7465 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0215 8.0 8 2.3570 0.006 1938.2804 1343.5136 368.0 570.0 0.6456 368.0 0.6456 81.0 81.0 158.0 0.5127 0.5127 106.0 106.0 152.0 0.6974 0.6974 106.0 106.0 142.0 0.7465 0.7465 75.0 75.0 118.0 0.6356 0.6356 0.0 0.0 0.0 0.0 0.0
0.0005 9.0 9 3.1356 0.006 2578.5187 1787.2930 373.0 570.0 0.6544 373.0 0.6544 87.0 87.0 158.0 0.5506 0.5506 106.0 106.0 152.0 0.6974 0.6974 103.0 103.0 142.0 0.7254 0.7254 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 10.0 10 3.6755 0.006 3022.5058 2095.0413 373.0 570.0 0.6544 373.0 0.6544 94.0 94.0 158.0 0.5949 0.5949 106.0 106.0 152.0 0.6974 0.6974 99.0 99.0 142.0 0.6972 0.6972 74.0 74.0 118.0 0.6271 0.6271 0.0 0.0 0.0 0.0 0.0
0.0 11.0 11 4.0632 0.006 3341.3365 2316.0380 374.0 570.0 0.6561 374.0 0.6561 99.0 99.0 158.0 0.6266 0.6266 106.0 106.0 152.0 0.6974 0.6974 97.0 97.0 142.0 0.6831 0.6831 72.0 72.0 118.0 0.6102 0.6102 0.0 0.0 0.0 0.0 0.0
0.0 12.0 12 4.4189 0.006 3633.8424 2518.7876 377.0 570.0 0.6614 377.0 0.6614 108.0 108.0 158.0 0.6835 0.6835 106.0 106.0 152.0 0.6974 0.6974 91.0 91.0 142.0 0.6408 0.6408 72.0 72.0 118.0 0.6102 0.6102 0.0 0.0 0.0 0.0 0.0
0.0 13.0 13 4.6996 0.006 3864.6172 2678.7485 375.0 570.0 0.6579 375.0 0.6579 110.0 110.0 158.0 0.6962 0.6962 106.0 106.0 152.0 0.6974 0.6974 88.0 88.0 142.0 0.6197 0.6197 71.0 71.0 118.0 0.6017 0.6017 0.0 0.0 0.0 0.0 0.0
0.0 14.0 14 4.9383 0.006 4060.9810 2814.8576 377.0 570.0 0.6614 377.0 0.6614 112.0 112.0 158.0 0.7089 0.7089 107.0 107.0 152.0 0.7039 0.7039 87.0 87.0 142.0 0.6127 0.6127 71.0 71.0 118.0 0.6017 0.6017 0.0 0.0 0.0 0.0 0.0
0.0 15.0 15 5.0850 0.006 4181.5466 2898.4273 377.0 570.0 0.6614 377.0 0.6614 113.0 113.0 158.0 0.7152 0.7152 107.0 107.0 152.0 0.7039 0.7039 86.0 86.0 142.0 0.6056 0.6056 71.0 71.0 118.0 0.6017 0.6017 0.0 0.0 0.0 0.0 0.0
0.0 16.0 16 5.2052 0.006 4280.4252 2966.9647 376.0 570.0 0.6596 376.0 0.6596 115.0 115.0 158.0 0.7278 0.7278 106.0 106.0 152.0 0.6974 0.6974 85.0 85.0 142.0 0.5986 0.5986 70.0 70.0 118.0 0.5932 0.5932 0.0 0.0 0.0 0.0 0.0
0.0 17.0 17 5.3576 0.006 4405.7475 3053.8314 375.0 570.0 0.6579 375.0 0.6579 115.0 115.0 158.0 0.7278 0.7278 105.0 105.0 152.0 0.6908 0.6908 85.0 85.0 142.0 0.5986 0.5986 70.0 70.0 118.0 0.5932 0.5932 0.0 0.0 0.0 0.0 0.0
0.0 18.0 18 5.4076 0.006 4446.8326 3082.3095 377.0 570.0 0.6614 377.0 0.6614 117.0 117.0 158.0 0.7405 0.7405 105.0 105.0 152.0 0.6908 0.6908 85.0 85.0 142.0 0.5986 0.5986 70.0 70.0 118.0 0.5932 0.5932 0.0 0.0 0.0 0.0 0.0
0.0 19.0 19 5.4880 0.006 4512.9806 3128.1598 376.0 570.0 0.6596 376.0 0.6596 117.0 117.0 158.0 0.7405 0.7405 105.0 105.0 152.0 0.6908 0.6908 85.0 85.0 142.0 0.5986 0.5986 69.0 69.0 118.0 0.5847 0.5847 0.0 0.0 0.0 0.0 0.0
0.0 20.0 20 5.5562 0.006 4569.0443 3167.0202 376.0 570.0 0.6596 376.0 0.6596 117.0 117.0 158.0 0.7405 0.7405 107.0 107.0 152.0 0.7039 0.7039 84.0 84.0 142.0 0.5915 0.5915 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 21.0 21 5.5900 0.006 4596.8882 3186.3201 374.0 570.0 0.6561 374.0 0.6561 117.0 117.0 158.0 0.7405 0.7405 105.0 105.0 152.0 0.6908 0.6908 84.0 84.0 142.0 0.5915 0.5915 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 22.0 22 5.5906 0.006 4597.3878 3186.6664 373.0 570.0 0.6544 373.0 0.6544 117.0 117.0 158.0 0.7405 0.7405 104.0 104.0 152.0 0.6842 0.6842 83.0 83.0 142.0 0.5845 0.5845 69.0 69.0 118.0 0.5847 0.5847 0.0 0.0 0.0 0.0 0.0
0.0 23.0 23 5.6844 0.006 4674.4978 3240.1150 372.0 570.0 0.6526 372.0 0.6526 115.0 115.0 158.0 0.7278 0.7278 106.0 106.0 152.0 0.6974 0.6974 83.0 83.0 142.0 0.5845 0.5845 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 24.0 24 5.6873 0.006 4676.8816 3241.7673 373.0 570.0 0.6544 373.0 0.6544 116.0 116.0 158.0 0.7342 0.7342 106.0 106.0 152.0 0.6974 0.6974 83.0 83.0 142.0 0.5845 0.5845 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 25.0 25 5.7152 0.006 4699.8429 3257.6828 373.0 570.0 0.6544 373.0 0.6544 118.0 118.0 158.0 0.7468 0.7468 104.0 104.0 152.0 0.6842 0.6842 83.0 83.0 142.0 0.5845 0.5845 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 26.0 26 5.7321 0.006 4713.7448 3267.3190 372.0 570.0 0.6526 372.0 0.6526 116.0 116.0 158.0 0.7342 0.7342 105.0 105.0 152.0 0.6908 0.6908 83.0 83.0 142.0 0.5845 0.5845 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 27.0 27 5.7109 0.006 4696.2915 3255.2212 374.0 570.0 0.6561 374.0 0.6561 117.0 117.0 158.0 0.7405 0.7405 106.0 106.0 152.0 0.6974 0.6974 83.0 83.0 142.0 0.5845 0.5845 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 28.0 28 5.7248 0.006 4707.7021 3263.1304 374.0 570.0 0.6561 374.0 0.6561 118.0 118.0 158.0 0.7468 0.7468 105.0 105.0 152.0 0.6908 0.6908 82.0 82.0 142.0 0.5775 0.5775 69.0 69.0 118.0 0.5847 0.5847 0.0 0.0 0.0 0.0 0.0
0.0 29.0 29 5.7557 0.006 4733.0906 3280.7284 371.0 570.0 0.6509 371.0 0.6509 116.0 116.0 158.0 0.7342 0.7342 106.0 106.0 152.0 0.6974 0.6974 82.0 82.0 142.0 0.5775 0.5775 67.0 67.0 118.0 0.5678 0.5678 0.0 0.0 0.0 0.0 0.0
0.0 30.0 30 5.7547 0.006 4732.2990 3280.1797 374.0 570.0 0.6561 374.0 0.6561 117.0 117.0 158.0 0.7405 0.7405 107.0 107.0 152.0 0.7039 0.7039 82.0 82.0 142.0 0.5775 0.5775 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 31.0 31 5.7254 0.006 4708.2410 3263.5040 376.0 570.0 0.6596 376.0 0.6596 118.0 118.0 158.0 0.7468 0.7468 107.0 107.0 152.0 0.7039 0.7039 83.0 83.0 142.0 0.5845 0.5845 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 32.0 32 5.7587 0.006 4735.5646 3282.4433 370.0 570.0 0.6491 370.0 0.6491 117.0 117.0 158.0 0.7405 0.7405 103.0 103.0 152.0 0.6776 0.6776 82.0 82.0 142.0 0.5775 0.5775 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 33.0 33 5.7623 0.006 4738.5261 3284.4960 373.0 570.0 0.6544 373.0 0.6544 118.0 118.0 158.0 0.7468 0.7468 105.0 105.0 152.0 0.6908 0.6908 82.0 82.0 142.0 0.5775 0.5775 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 34.0 34 5.7341 0.006 4715.3508 3268.4321 375.0 570.0 0.6579 375.0 0.6579 119.0 119.0 158.0 0.7532 0.7532 105.0 105.0 152.0 0.6908 0.6908 83.0 83.0 142.0 0.5845 0.5845 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 35.0 35 5.7825 0.006 4755.1337 3296.0075 371.0 570.0 0.6509 371.0 0.6509 117.0 117.0 158.0 0.7405 0.7405 104.0 104.0 152.0 0.6842 0.6842 82.0 82.0 142.0 0.5775 0.5775 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 36.0 36 5.7274 0.006 4709.8409 3264.6129 373.0 570.0 0.6544 373.0 0.6544 118.0 118.0 158.0 0.7468 0.7468 104.0 104.0 152.0 0.6842 0.6842 83.0 83.0 142.0 0.5845 0.5845 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 37.0 37 5.7777 0.006 4751.2280 3293.3003 370.0 570.0 0.6491 370.0 0.6491 117.0 117.0 158.0 0.7405 0.7405 103.0 103.0 152.0 0.6776 0.6776 82.0 82.0 142.0 0.5775 0.5775 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 38.0 38 5.7209 0.006 4704.5101 3260.9179 373.0 570.0 0.6544 373.0 0.6544 117.0 117.0 158.0 0.7405 0.7405 105.0 105.0 152.0 0.6908 0.6908 83.0 83.0 142.0 0.5845 0.5845 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 39.0 39 5.7626 0.006 4738.7797 3284.6718 373.0 570.0 0.6544 373.0 0.6544 118.0 118.0 158.0 0.7468 0.7468 105.0 105.0 152.0 0.6908 0.6908 82.0 82.0 142.0 0.5775 0.5775 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 40.0 40 5.7875 0.006 4759.2782 3298.8802 374.0 570.0 0.6561 374.0 0.6561 118.0 118.0 158.0 0.7468 0.7468 106.0 106.0 152.0 0.6974 0.6974 82.0 82.0 142.0 0.5775 0.5775 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 41.0 41 5.7475 0.006 4726.3976 3276.0891 376.0 570.0 0.6596 376.0 0.6596 120.0 120.0 158.0 0.7595 0.7595 106.0 106.0 152.0 0.6974 0.6974 82.0 82.0 142.0 0.5775 0.5775 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0 42.0 42 5.7711 0.006 4745.7636 3289.5126 373.0 570.0 0.6544 373.0 0.6544 118.0 118.0 158.0 0.7468 0.7468 105.0 105.0 152.0 0.6908 0.6908 82.0 82.0 142.0 0.5775 0.5775 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0

Framework versions

  • Transformers 4.51.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.5.0
  • Tokenizers 0.21.1
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