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ARC-Challenge_Llama-3.2-1B-5v3zw441

Text Generation
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llama
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text-generation-inference
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ARC-Challenge_Llama-3.2-1B-5v3zw441

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: 9.1702
  • Model Preparation Time: 0.0072
  • Mdl: 3955.7186
  • Accumulated Loss: 2741.8952
  • Correct Preds: 92.0
  • Total Preds: 299.0
  • Accuracy: 0.3077
  • Correct Gen Preds: 65.0
  • Gen Accuracy: 0.2174
  • Correct Gen Preds 32: 0.0
  • Correct Preds 32: 0.0
  • Total Labels 32: 64.0
  • Accuracy 32: 0.0
  • Gen Accuracy 32: 0.0
  • Correct Gen Preds 33: 16.0
  • Correct Preds 33: 35.0
  • Total Labels 33: 73.0
  • Accuracy 33: 0.4795
  • Gen Accuracy 33: 0.2192
  • Correct Gen Preds 34: 45.0
  • Correct Preds 34: 52.0
  • Total Labels 34: 78.0
  • Accuracy 34: 0.6667
  • Gen Accuracy 34: 0.5769
  • Correct Gen Preds 35: 4.0
  • Correct Preds 35: 5.0
  • Total Labels 35: 83.0
  • Accuracy 35: 0.0602
  • Gen Accuracy 35: 0.0482
  • Correct Gen Preds 36: 0.0
  • Correct Preds 36: 0.0
  • Total Labels 36: 1.0
  • 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 1.6389 0.0072 706.9523 490.0220 66.0 299.0 0.2207 66.0 0.2207 62.0 62.0 64.0 0.9688 0.9688 0.0 0.0 73.0 0.0 0.0 4.0 4.0 78.0 0.0513 0.0513 0.0 0.0 83.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
1.8059 1.0 1 1.6389 0.0072 706.9523 490.0220 66.0 299.0 0.2207 66.0 0.2207 62.0 62.0 64.0 0.9688 0.9688 0.0 0.0 73.0 0.0 0.0 4.0 4.0 78.0 0.0513 0.0513 0.0 0.0 83.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
1.8059 2.0 2 2.3768 0.0072 1025.2880 710.6755 73.0 299.0 0.2441 73.0 0.2441 0.0 0.0 64.0 0.0 0.0 73.0 73.0 73.0 1.0 1.0 0.0 0.0 78.0 0.0 0.0 0.0 0.0 83.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
1.4788 3.0 3 1.5504 0.0072 668.7983 463.5757 88.0 299.0 0.2943 87.0 0.2910 0.0 0.0 64.0 0.0 0.0 20.0 21.0 73.0 0.2877 0.2740 52.0 52.0 78.0 0.6667 0.6667 15.0 15.0 83.0 0.1807 0.1807 0.0 0.0 1.0 0.0 0.0
0.8828 4.0 4 1.8422 0.0072 794.6544 550.8124 71.0 299.0 0.2375 51.0 0.1706 1.0 4.0 64.0 0.0625 0.0156 49.0 65.0 73.0 0.8904 0.6712 1.0 2.0 78.0 0.0256 0.0128 0.0 0.0 83.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
0.2786 5.0 5 2.3001 0.0072 992.1852 687.7304 82.0 299.0 0.2742 77.0 0.2575 0.0 2.0 64.0 0.0312 0.0 66.0 69.0 73.0 0.9452 0.9041 10.0 10.0 78.0 0.1282 0.1282 1.0 1.0 83.0 0.0120 0.0120 0.0 0.0 1.0 0.0 0.0
0.0357 6.0 6 3.5008 0.0072 1510.1111 1046.7292 76.0 299.0 0.2542 60.0 0.2007 0.0 1.0 64.0 0.0156 0.0 37.0 51.0 73.0 0.6986 0.5068 21.0 21.0 78.0 0.2692 0.2692 2.0 3.0 83.0 0.0361 0.0241 0.0 0.0 1.0 0.0 0.0
0.0004 7.0 7 4.9923 0.0072 2153.5282 1492.7120 83.0 299.0 0.2776 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 35.0 48.0 73.0 0.6575 0.4795 29.0 32.0 78.0 0.4103 0.3718 2.0 3.0 83.0 0.0361 0.0241 0.0 0.0 1.0 0.0 0.0
0.0 8.0 8 6.1504 0.0072 2653.0578 1838.9595 91.0 299.0 0.3043 72.0 0.2408 0.0 0.0 64.0 0.0 0.0 33.0 46.0 73.0 0.6301 0.4521 37.0 40.0 78.0 0.5128 0.4744 2.0 5.0 83.0 0.0602 0.0241 0.0 0.0 1.0 0.0 0.0
0.0 9.0 9 6.9987 0.0072 3018.9984 2092.6102 90.0 299.0 0.3010 70.0 0.2341 0.0 0.0 64.0 0.0 0.0 29.0 42.0 73.0 0.5753 0.3973 39.0 44.0 78.0 0.5641 0.5 2.0 4.0 83.0 0.0482 0.0241 0.0 0.0 1.0 0.0 0.0
0.0 10.0 10 7.6103 0.0072 3282.8440 2275.4941 90.0 299.0 0.3010 69.0 0.2308 0.0 0.0 64.0 0.0 0.0 24.0 39.0 73.0 0.5342 0.3288 42.0 45.0 78.0 0.5769 0.5385 3.0 6.0 83.0 0.0723 0.0361 0.0 0.0 1.0 0.0 0.0
0.0 11.0 11 8.0287 0.0072 3463.2859 2400.5668 88.0 299.0 0.2943 70.0 0.2341 0.0 0.0 64.0 0.0 0.0 23.0 35.0 73.0 0.4795 0.3151 44.0 48.0 78.0 0.6154 0.5641 3.0 5.0 83.0 0.0602 0.0361 0.0 0.0 1.0 0.0 0.0
0.0 12.0 12 8.4104 0.0072 3627.9392 2514.6958 88.0 299.0 0.2943 67.0 0.2241 0.0 0.0 64.0 0.0 0.0 22.0 34.0 73.0 0.4658 0.3014 42.0 49.0 78.0 0.6282 0.5385 3.0 5.0 83.0 0.0602 0.0361 0.0 0.0 1.0 0.0 0.0
0.0 13.0 13 8.6021 0.0072 3710.6622 2572.0350 91.0 299.0 0.3043 70.0 0.2341 0.0 0.0 64.0 0.0 0.0 23.0 35.0 73.0 0.4795 0.3151 44.0 50.0 78.0 0.6410 0.5641 3.0 6.0 83.0 0.0723 0.0361 0.0 0.0 1.0 0.0 0.0
0.0 14.0 14 8.7289 0.0072 3765.3495 2609.9414 91.0 299.0 0.3043 69.0 0.2308 0.0 0.0 64.0 0.0 0.0 22.0 36.0 73.0 0.4932 0.3014 43.0 49.0 78.0 0.6282 0.5513 4.0 6.0 83.0 0.0723 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 15.0 15 8.7814 0.0072 3788.0128 2625.6504 91.0 299.0 0.3043 68.0 0.2274 0.0 0.0 64.0 0.0 0.0 20.0 35.0 73.0 0.4795 0.2740 44.0 50.0 78.0 0.6410 0.5641 4.0 6.0 83.0 0.0723 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 16.0 16 8.8823 0.0072 3831.5105 2655.8007 90.0 299.0 0.3010 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 18.0 35.0 73.0 0.4795 0.2466 44.0 49.0 78.0 0.6282 0.5641 4.0 6.0 83.0 0.0723 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 17.0 17 8.9496 0.0072 3860.5383 2675.9212 89.0 299.0 0.2977 65.0 0.2174 0.0 0.0 64.0 0.0 0.0 17.0 35.0 73.0 0.4795 0.2329 44.0 49.0 78.0 0.6282 0.5641 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 18.0 18 9.0121 0.0072 3887.5219 2694.6248 91.0 299.0 0.3043 64.0 0.2140 0.0 0.0 64.0 0.0 0.0 15.0 35.0 73.0 0.4795 0.2055 45.0 50.0 78.0 0.6410 0.5769 4.0 6.0 83.0 0.0723 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 19.0 19 9.0640 0.0072 3909.8990 2710.1354 90.0 299.0 0.3010 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 46.0 50.0 78.0 0.6410 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 20.0 20 9.0766 0.0072 3915.3382 2713.9056 90.0 299.0 0.3010 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 46.0 50.0 78.0 0.6410 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 21.0 21 9.1070 0.0072 3928.4656 2723.0049 90.0 299.0 0.3010 65.0 0.2174 0.0 0.0 64.0 0.0 0.0 15.0 35.0 73.0 0.4795 0.2055 46.0 50.0 78.0 0.6410 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 22.0 22 9.1207 0.0072 3934.3616 2727.0917 91.0 299.0 0.3043 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 17.0 35.0 73.0 0.4795 0.2329 45.0 51.0 78.0 0.6538 0.5769 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 23.0 23 9.1378 0.0072 3941.7153 2732.1888 91.0 299.0 0.3043 64.0 0.2140 0.0 0.0 64.0 0.0 0.0 15.0 35.0 73.0 0.4795 0.2055 45.0 51.0 78.0 0.6538 0.5769 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 24.0 24 9.1702 0.0072 3955.7186 2741.8952 92.0 299.0 0.3077 65.0 0.2174 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 45.0 52.0 78.0 0.6667 0.5769 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 25.0 25 9.1845 0.0072 3961.8830 2746.1680 92.0 299.0 0.3077 65.0 0.2174 0.0 0.0 64.0 0.0 0.0 15.0 35.0 73.0 0.4795 0.2055 46.0 52.0 78.0 0.6667 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 26.0 26 9.2133 0.0072 3974.2828 2754.7629 90.0 299.0 0.3010 65.0 0.2174 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 45.0 50.0 78.0 0.6410 0.5769 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 27.0 27 9.1654 0.0072 3953.6505 2740.4617 92.0 299.0 0.3077 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 46.0 52.0 78.0 0.6667 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 28.0 28 9.1935 0.0072 3965.7623 2748.8569 92.0 299.0 0.3077 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 17.0 35.0 73.0 0.4795 0.2329 45.0 52.0 78.0 0.6667 0.5769 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 29.0 29 9.1695 0.0072 3955.4172 2741.6863 92.0 299.0 0.3077 67.0 0.2241 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 47.0 52.0 78.0 0.6667 0.6026 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 30.0 30 9.1911 0.0072 3964.7189 2748.1337 92.0 299.0 0.3077 65.0 0.2174 0.0 0.0 64.0 0.0 0.0 15.0 35.0 73.0 0.4795 0.2055 46.0 52.0 78.0 0.6667 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 31.0 31 9.1985 0.0072 3967.9350 2750.3630 91.0 299.0 0.3043 67.0 0.2241 0.0 0.0 64.0 0.0 0.0 17.0 35.0 73.0 0.4795 0.2329 46.0 51.0 78.0 0.6538 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 32.0 32 9.1956 0.0072 3966.6719 2749.4874 92.0 299.0 0.3077 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 46.0 52.0 78.0 0.6667 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 33.0 33 9.2326 0.0072 3982.6086 2760.5339 91.0 299.0 0.3043 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 46.0 51.0 78.0 0.6538 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 34.0 34 9.1921 0.0072 3965.1599 2748.4394 92.0 299.0 0.3077 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 15.0 35.0 73.0 0.4795 0.2055 47.0 52.0 78.0 0.6667 0.6026 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 35.0 35 9.2354 0.0072 3983.8575 2761.3996 91.0 299.0 0.3043 65.0 0.2174 0.0 0.0 64.0 0.0 0.0 15.0 35.0 73.0 0.4795 0.2055 46.0 51.0 78.0 0.6538 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 36.0 36 9.2211 0.0072 3977.6828 2757.1196 91.0 299.0 0.3043 65.0 0.2174 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 45.0 51.0 78.0 0.6538 0.5769 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 37.0 37 9.2311 0.0072 3981.9701 2760.0914 91.0 299.0 0.3043 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 16.0 34.0 73.0 0.4658 0.2192 46.0 52.0 78.0 0.6667 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 38.0 38 9.2726 0.0072 3999.8736 2772.5011 91.0 299.0 0.3043 67.0 0.2241 0.0 0.0 64.0 0.0 0.0 16.0 34.0 73.0 0.4658 0.2192 47.0 52.0 78.0 0.6667 0.6026 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 39.0 39 9.2677 0.0072 3997.7545 2771.0322 92.0 299.0 0.3077 67.0 0.2241 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 47.0 52.0 78.0 0.6667 0.6026 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 40.0 40 9.2004 0.0072 3968.7216 2750.9082 92.0 299.0 0.3077 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 46.0 52.0 78.0 0.6667 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 41.0 41 9.2552 0.0072 3992.3608 2767.2936 92.0 299.0 0.3077 65.0 0.2174 0.0 0.0 64.0 0.0 0.0 15.0 35.0 73.0 0.4795 0.2055 46.0 52.0 78.0 0.6667 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 42.0 42 9.2246 0.0072 3979.1888 2758.1635 92.0 299.0 0.3077 65.0 0.2174 0.0 0.0 64.0 0.0 0.0 15.0 35.0 73.0 0.4795 0.2055 46.0 52.0 78.0 0.6667 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 43.0 43 9.2157 0.0072 3975.3528 2755.5046 91.0 299.0 0.3043 69.0 0.2308 0.0 0.0 64.0 0.0 0.0 18.0 35.0 73.0 0.4795 0.2466 47.0 51.0 78.0 0.6538 0.6026 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 44.0 44 9.2293 0.0072 3981.2013 2759.5585 91.0 299.0 0.3043 68.0 0.2274 0.0 0.0 64.0 0.0 0.0 17.0 34.0 73.0 0.4658 0.2329 47.0 52.0 78.0 0.6667 0.6026 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 45.0 45 9.2613 0.0072 3994.9932 2769.1183 92.0 299.0 0.3077 68.0 0.2274 0.0 0.0 64.0 0.0 0.0 17.0 35.0 73.0 0.4795 0.2329 47.0 52.0 78.0 0.6667 0.6026 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 46.0 46 9.2348 0.0072 3983.5662 2761.1977 91.0 299.0 0.3043 68.0 0.2274 0.0 0.0 64.0 0.0 0.0 17.0 34.0 73.0 0.4658 0.2329 47.0 52.0 78.0 0.6667 0.6026 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 47.0 47 9.2317 0.0072 3982.2565 2760.2899 92.0 299.0 0.3077 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 46.0 52.0 78.0 0.6667 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 48.0 48 9.2409 0.0072 3986.1969 2763.0211 92.0 299.0 0.3077 67.0 0.2241 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 47.0 52.0 78.0 0.6667 0.6026 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 49.0 49 9.2229 0.0072 3978.4535 2757.6538 91.0 299.0 0.3043 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 15.0 34.0 73.0 0.4658 0.2055 47.0 52.0 78.0 0.6667 0.6026 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 50.0 50 9.2700 0.0072 3998.7541 2771.7251 91.0 299.0 0.3043 68.0 0.2274 0.0 0.0 64.0 0.0 0.0 18.0 34.0 73.0 0.4658 0.2466 46.0 52.0 78.0 0.6667 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 51.0 51 9.2386 0.0072 3985.2168 2762.3418 92.0 299.0 0.3077 67.0 0.2241 0.0 0.0 64.0 0.0 0.0 17.0 35.0 73.0 0.4795 0.2329 46.0 52.0 78.0 0.6667 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 52.0 52 9.2318 0.0072 3982.2619 2760.2936 92.0 299.0 0.3077 67.0 0.2241 0.0 0.0 64.0 0.0 0.0 16.0 35.0 73.0 0.4795 0.2192 47.0 52.0 78.0 0.6667 0.6026 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 53.0 53 9.2348 0.0072 3983.5704 2761.2006 91.0 299.0 0.3043 69.0 0.2308 0.0 0.0 64.0 0.0 0.0 18.0 34.0 73.0 0.4658 0.2466 47.0 52.0 78.0 0.6667 0.6026 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 0.0 0.0
0.0 54.0 54 9.2415 0.0072 3986.4454 2763.1934 91.0 299.0 0.3043 69.0 0.2308 0.0 0.0 64.0 0.0 0.0 19.0 34.0 73.0 0.4658 0.2603 46.0 52.0 78.0 0.6667 0.5897 4.0 5.0 83.0 0.0602 0.0482 0.0 0.0 1.0 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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