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ARC-Easy_Llama-3.2-1B-nuliteno

Text Generation
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llama
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text-generation-inference
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ARC-Easy_Llama-3.2-1B-nuliteno

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: 1.2368
  • Model Preparation Time: 0.0057
  • Mdl: 1017.0858
  • Accumulated Loss: 704.9901
  • Correct Preds: 403.0
  • Total Preds: 570.0
  • Accuracy: 0.7070
  • Correct Gen Preds: 380.0
  • Gen Accuracy: 0.6667
  • Correct Gen Preds 32: 115.0
  • Correct Preds 32: 123.0
  • Total Labels 32: 158.0
  • Accuracy 32: 0.7785
  • Gen Accuracy 32: 0.7278
  • Correct Gen Preds 33: 101.0
  • Correct Preds 33: 104.0
  • Total Labels 33: 152.0
  • Accuracy 33: 0.6842
  • Gen Accuracy 33: 0.6645
  • Correct Gen Preds 34: 98.0
  • Correct Preds 34: 106.0
  • Total Labels 34: 142.0
  • Accuracy 34: 0.7465
  • Gen Accuracy 34: 0.6901
  • Correct Gen Preds 35: 66.0
  • Correct Preds 35: 70.0
  • Total Labels 35: 118.0
  • Accuracy 35: 0.5932
  • Gen Accuracy 35: 0.5593
  • 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: constant
  • lr_scheduler_warmup_ratio: 0.001
  • 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 0.0057 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
1.0495 1.0 10 1.1252 0.0057 925.2995 641.3687 365.0 570.0 0.6404 346.0 0.6070 114.0 123.0 158.0 0.7785 0.7215 87.0 90.0 152.0 0.5921 0.5724 90.0 95.0 142.0 0.6690 0.6338 55.0 57.0 118.0 0.4831 0.4661 0.0 0.0 0.0 0.0 0.0
0.5032 2.0 20 0.9410 0.0057 773.8220 536.3725 390.0 570.0 0.6842 390.0 0.6842 109.0 109.0 158.0 0.6899 0.6899 101.0 101.0 152.0 0.6645 0.6645 108.0 108.0 142.0 0.7606 0.7606 72.0 72.0 118.0 0.6102 0.6102 0.0 0.0 0.0 0.0 0.0
0.2482 3.0 30 1.2368 0.0057 1017.0858 704.9901 403.0 570.0 0.7070 380.0 0.6667 115.0 123.0 158.0 0.7785 0.7278 101.0 104.0 152.0 0.6842 0.6645 98.0 106.0 142.0 0.7465 0.6901 66.0 70.0 118.0 0.5932 0.5593 0.0 0.0 0.0 0.0 0.0
0.0062 4.0 40 1.7542 0.0057 1442.5071 999.8697 397.0 570.0 0.6965 364.0 0.6386 102.0 115.0 158.0 0.7278 0.6456 98.0 111.0 152.0 0.7303 0.6447 94.0 101.0 142.0 0.7113 0.6620 70.0 70.0 118.0 0.5932 0.5932 0.0 0.0 0.0 0.0 0.0
0.0184 5.0 50 2.8819 0.0057 2369.9259 1642.7075 381.0 570.0 0.6684 380.0 0.6667 81.0 82.0 158.0 0.5190 0.5127 109.0 109.0 152.0 0.7171 0.7171 111.0 111.0 142.0 0.7817 0.7817 79.0 79.0 118.0 0.6695 0.6695 0.0 0.0 0.0 0.0 0.0
0.0001 6.0 60 2.7818 0.0057 2287.5613 1585.6166 395.0 570.0 0.6930 392.0 0.6877 116.0 118.0 158.0 0.7468 0.7342 107.0 108.0 152.0 0.7105 0.7039 104.0 104.0 142.0 0.7324 0.7324 65.0 65.0 118.0 0.5508 0.5508 0.0 0.0 0.0 0.0 0.0
0.0001 7.0 70 3.0788 0.0057 2531.7691 1754.8886 394.0 570.0 0.6912 371.0 0.6509 99.0 111.0 158.0 0.7025 0.6266 103.0 107.0 152.0 0.7039 0.6776 101.0 107.0 142.0 0.7535 0.7113 68.0 69.0 118.0 0.5847 0.5763 0.0 0.0 0.0 0.0 0.0
0.418 8.0 80 3.0952 0.0057 2545.3063 1764.2719 393.0 570.0 0.6895 388.0 0.6807 110.0 114.0 158.0 0.7215 0.6962 108.0 108.0 152.0 0.7105 0.7105 103.0 104.0 142.0 0.7324 0.7254 67.0 67.0 118.0 0.5678 0.5678 0.0 0.0 0.0 0.0 0.0
0.0001 9.0 90 2.8307 0.0057 2327.7607 1613.4808 393.0 570.0 0.6895 387.0 0.6789 115.0 117.0 158.0 0.7405 0.7278 106.0 107.0 152.0 0.7039 0.6974 103.0 104.0 142.0 0.7324 0.7254 63.0 65.0 118.0 0.5508 0.5339 0.0 0.0 0.0 0.0 0.0
0.0001 10.0 100 2.8407 0.0057 2336.0188 1619.2048 394.0 570.0 0.6912 389.0 0.6825 115.0 117.0 158.0 0.7405 0.7278 106.0 107.0 152.0 0.7039 0.6974 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0001 11.0 110 2.9273 0.0057 2407.1993 1668.5434 390.0 570.0 0.6842 384.0 0.6737 113.0 116.0 158.0 0.7342 0.7152 106.0 107.0 152.0 0.7039 0.6974 102.0 103.0 142.0 0.7254 0.7183 63.0 64.0 118.0 0.5424 0.5339 0.0 0.0 0.0 0.0 0.0
0.0 12.0 120 2.9689 0.0057 2441.4194 1692.2630 393.0 570.0 0.6895 387.0 0.6789 114.0 117.0 158.0 0.7405 0.7215 105.0 106.0 152.0 0.6974 0.6908 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 13.0 130 3.0274 0.0057 2489.5532 1725.6268 393.0 570.0 0.6895 387.0 0.6789 114.0 117.0 158.0 0.7405 0.7215 105.0 106.0 152.0 0.6974 0.6908 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 14.0 140 3.0474 0.0057 2505.9722 1737.0076 393.0 570.0 0.6895 387.0 0.6789 113.0 116.0 158.0 0.7342 0.7152 106.0 107.0 152.0 0.7039 0.6974 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 15.0 150 3.0533 0.0057 2510.8689 1740.4017 394.0 570.0 0.6912 388.0 0.6807 114.0 117.0 158.0 0.7405 0.7215 106.0 107.0 152.0 0.7039 0.6974 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 16.0 160 3.0858 0.0057 2537.5315 1758.8828 393.0 570.0 0.6895 388.0 0.6807 113.0 116.0 158.0 0.7342 0.7152 107.0 107.0 152.0 0.7039 0.7039 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 17.0 170 3.1061 0.0057 2554.2536 1770.4737 392.0 570.0 0.6877 386.0 0.6772 112.0 115.0 158.0 0.7278 0.7089 107.0 108.0 152.0 0.7105 0.7039 102.0 103.0 142.0 0.7254 0.7183 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 18.0 180 3.1204 0.0057 2566.0528 1778.6522 392.0 570.0 0.6877 386.0 0.6772 112.0 115.0 158.0 0.7278 0.7089 107.0 108.0 152.0 0.7105 0.7039 102.0 103.0 142.0 0.7254 0.7183 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 19.0 190 3.1196 0.0057 2565.3768 1778.1837 392.0 570.0 0.6877 386.0 0.6772 113.0 116.0 158.0 0.7342 0.7152 106.0 107.0 152.0 0.7039 0.6974 102.0 103.0 142.0 0.7254 0.7183 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 20.0 200 3.1321 0.0057 2575.6468 1785.3023 390.0 570.0 0.6842 384.0 0.6737 112.0 115.0 158.0 0.7278 0.7089 105.0 106.0 152.0 0.6974 0.6908 102.0 103.0 142.0 0.7254 0.7183 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 21.0 210 3.1534 0.0057 2593.1725 1797.4502 391.0 570.0 0.6860 386.0 0.6772 113.0 115.0 158.0 0.7278 0.7152 106.0 107.0 152.0 0.7039 0.6974 102.0 103.0 142.0 0.7254 0.7183 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 22.0 220 3.1442 0.0057 2585.5666 1792.1782 392.0 570.0 0.6877 386.0 0.6772 112.0 115.0 158.0 0.7278 0.7089 106.0 107.0 152.0 0.7039 0.6974 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 23.0 230 3.1591 0.0057 2597.8408 1800.6860 392.0 570.0 0.6877 386.0 0.6772 113.0 116.0 158.0 0.7342 0.7152 105.0 106.0 152.0 0.6974 0.6908 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 24.0 240 3.1706 0.0057 2607.2820 1807.2301 391.0 570.0 0.6860 385.0 0.6754 111.0 114.0 158.0 0.7215 0.7025 106.0 107.0 152.0 0.7039 0.6974 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 25.0 250 3.1782 0.0057 2613.5598 1811.5816 391.0 570.0 0.6860 385.0 0.6754 111.0 114.0 158.0 0.7215 0.7025 106.0 107.0 152.0 0.7039 0.6974 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 26.0 260 3.1695 0.0057 2606.4207 1806.6332 393.0 570.0 0.6895 387.0 0.6789 114.0 117.0 158.0 0.7405 0.7215 105.0 106.0 152.0 0.6974 0.6908 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 27.0 270 3.1835 0.0057 2617.9143 1814.5999 393.0 570.0 0.6895 388.0 0.6807 115.0 117.0 158.0 0.7405 0.7278 105.0 106.0 152.0 0.6974 0.6908 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 28.0 280 3.1995 0.0057 2631.0951 1823.7361 391.0 570.0 0.6860 385.0 0.6754 113.0 116.0 158.0 0.7342 0.7152 105.0 106.0 152.0 0.6974 0.6908 102.0 103.0 142.0 0.7254 0.7183 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 29.0 290 3.2063 0.0057 2636.6248 1827.5690 392.0 570.0 0.6877 386.0 0.6772 113.0 116.0 158.0 0.7342 0.7152 106.0 107.0 152.0 0.7039 0.6974 102.0 103.0 142.0 0.7254 0.7183 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 30.0 300 3.2030 0.0057 2633.9546 1825.7182 390.0 570.0 0.6842 384.0 0.6737 112.0 115.0 158.0 0.7278 0.7089 105.0 106.0 152.0 0.6974 0.6908 102.0 103.0 142.0 0.7254 0.7183 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 31.0 310 3.2200 0.0057 2647.9446 1835.4153 390.0 570.0 0.6842 384.0 0.6737 112.0 115.0 158.0 0.7278 0.7089 105.0 106.0 152.0 0.6974 0.6908 102.0 103.0 142.0 0.7254 0.7183 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 32.0 320 3.2230 0.0057 2650.4148 1837.1275 391.0 570.0 0.6860 385.0 0.6754 112.0 115.0 158.0 0.7278 0.7089 105.0 106.0 152.0 0.6974 0.6908 102.0 103.0 142.0 0.7254 0.7183 66.0 67.0 118.0 0.5678 0.5593 0.0 0.0 0.0 0.0 0.0
0.0 33.0 330 3.2226 0.0057 2650.0326 1836.8627 392.0 570.0 0.6877 387.0 0.6789 111.0 114.0 158.0 0.7215 0.7025 108.0 108.0 152.0 0.7105 0.7105 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 34.0 340 3.2358 0.0057 2660.8781 1844.3802 391.0 570.0 0.6860 385.0 0.6754 113.0 116.0 158.0 0.7342 0.7152 105.0 106.0 152.0 0.6974 0.6908 102.0 103.0 142.0 0.7254 0.7183 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 35.0 350 3.2371 0.0057 2661.9610 1845.1308 392.0 570.0 0.6877 387.0 0.6789 112.0 115.0 158.0 0.7278 0.7089 107.0 107.0 152.0 0.7039 0.7039 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 36.0 360 3.2345 0.0057 2659.8829 1843.6904 392.0 570.0 0.6877 386.0 0.6772 113.0 116.0 158.0 0.7342 0.7152 106.0 107.0 152.0 0.7039 0.6974 102.0 103.0 142.0 0.7254 0.7183 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 37.0 370 3.2478 0.0057 2670.7641 1851.2326 392.0 570.0 0.6877 386.0 0.6772 112.0 115.0 158.0 0.7278 0.7089 105.0 106.0 152.0 0.6974 0.6908 103.0 104.0 142.0 0.7324 0.7254 66.0 67.0 118.0 0.5678 0.5593 0.0 0.0 0.0 0.0 0.0
0.0 38.0 380 3.2443 0.0057 2667.8917 1849.2416 392.0 570.0 0.6877 386.0 0.6772 112.0 115.0 158.0 0.7278 0.7089 106.0 107.0 152.0 0.7039 0.6974 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 0.0 0.0 0.0 0.0 0.0
0.0 39.0 390 3.2590 0.0057 2680.0247 1857.6515 392.0 570.0 0.6877 386.0 0.6772 112.0 115.0 158.0 0.7278 0.7089 106.0 107.0 152.0 0.7039 0.6974 103.0 104.0 142.0 0.7324 0.7254 65.0 66.0 118.0 0.5593 0.5508 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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