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ARC-Easy_Llama-3.2-1B-622krk4g

Text Generation
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llama
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text-generation-inference
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ARC-Easy_Llama-3.2-1B-622krk4g

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: 3.0132
  • Model Preparation Time: 0.0057
  • Mdl: 2477.8389
  • Accumulated Loss: 1717.5071
  • Correct Preds: 407.0
  • Total Preds: 570.0
  • Accuracy: 0.7140
  • Correct Gen Preds: 407.0
  • Gen Accuracy: 0.7140
  • Correct Gen Preds 32: 104.0
  • Correct Preds 32: 104.0
  • Total Labels 32: 158.0
  • Accuracy 32: 0.6582
  • Gen Accuracy 32: 0.6582
  • Correct Gen Preds 33: 118.0
  • Correct Preds 33: 118.0
  • Total Labels 33: 152.0
  • Accuracy 33: 0.7763
  • Gen Accuracy 33: 0.7763
  • Correct Gen Preds 34: 103.0
  • Correct Preds 34: 103.0
  • Total Labels 34: 142.0
  • Accuracy 34: 0.7254
  • Gen Accuracy 34: 0.7254
  • Correct Gen Preds 35: 82.0
  • Correct Preds 35: 82.0
  • Total Labels 35: 118.0
  • Accuracy 35: 0.6949
  • Gen Accuracy 35: 0.6949
  • 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 0.0057 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
0.5128 1.0 9 1.2716 0.0057 1045.6621 724.7978 365.0 570.0 0.6404 365.0 0.6404 66.0 66.0 158.0 0.4177 0.4177 90.0 90.0 152.0 0.5921 0.5921 121.0 121.0 142.0 0.8521 0.8521 88.0 88.0 118.0 0.7458 0.7458 0.0 0.0 0.0 0.0 0.0
1.0744 2.0 18 1.1638 0.0057 957.0666 663.3880 390.0 570.0 0.6842 388.0 0.6807 89.0 91.0 158.0 0.5759 0.5633 109.0 109.0 152.0 0.7171 0.7171 114.0 114.0 142.0 0.8028 0.8028 76.0 76.0 118.0 0.6441 0.6441 0.0 0.0 0.0 0.0 0.0
0.0722 3.0 27 0.9485 0.0057 779.9649 540.6305 403.0 570.0 0.7070 403.0 0.7070 112.0 112.0 158.0 0.7089 0.7089 120.0 120.0 152.0 0.7895 0.7895 103.0 103.0 142.0 0.7254 0.7254 68.0 68.0 118.0 0.5763 0.5763 0.0 0.0 0.0 0.0 0.0
0.0006 4.0 36 1.9392 0.0057 1594.6584 1105.3330 404.0 570.0 0.7088 404.0 0.7088 116.0 116.0 158.0 0.7342 0.7342 106.0 106.0 152.0 0.6974 0.6974 105.0 105.0 142.0 0.7394 0.7394 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0016 5.0 45 2.1923 0.0057 1802.7670 1249.5829 404.0 570.0 0.7088 403.0 0.7070 112.0 113.0 158.0 0.7152 0.7089 102.0 102.0 152.0 0.6711 0.6711 106.0 106.0 142.0 0.7465 0.7465 83.0 83.0 118.0 0.7034 0.7034 0.0 0.0 0.0 0.0 0.0
0.0001 6.0 54 3.0132 0.0057 2477.8389 1717.5071 407.0 570.0 0.7140 407.0 0.7140 104.0 104.0 158.0 0.6582 0.6582 118.0 118.0 152.0 0.7763 0.7763 103.0 103.0 142.0 0.7254 0.7254 82.0 82.0 118.0 0.6949 0.6949 0.0 0.0 0.0 0.0 0.0
0.0 7.0 63 3.1848 0.0057 2618.9628 1815.3267 399.0 570.0 0.7 399.0 0.7 99.0 99.0 158.0 0.6266 0.6266 117.0 117.0 152.0 0.7697 0.7697 101.0 101.0 142.0 0.7113 0.7113 82.0 82.0 118.0 0.6949 0.6949 0.0 0.0 0.0 0.0 0.0
0.0 8.0 72 3.3835 0.0057 2782.3849 1928.6022 398.0 570.0 0.6982 398.0 0.6982 102.0 102.0 158.0 0.6456 0.6456 116.0 116.0 152.0 0.7632 0.7632 107.0 107.0 142.0 0.7535 0.7535 73.0 73.0 118.0 0.6186 0.6186 0.0 0.0 0.0 0.0 0.0
0.0 9.0 81 3.4467 0.0057 2834.3074 1964.5922 401.0 570.0 0.7035 400.0 0.7018 102.0 103.0 158.0 0.6519 0.6456 115.0 115.0 152.0 0.7566 0.7566 107.0 107.0 142.0 0.7535 0.7535 76.0 76.0 118.0 0.6441 0.6441 0.0 0.0 0.0 0.0 0.0
0.0 10.0 90 3.5037 0.0057 2881.2031 1997.0978 397.0 570.0 0.6965 396.0 0.6947 102.0 103.0 158.0 0.6519 0.6456 111.0 111.0 152.0 0.7303 0.7303 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
0.0 11.0 99 3.4913 0.0057 2871.0161 1990.0367 401.0 570.0 0.7035 400.0 0.7018 103.0 104.0 158.0 0.6582 0.6519 113.0 113.0 152.0 0.7434 0.7434 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 12.0 108 3.5206 0.0057 2895.1423 2006.7597 399.0 570.0 0.7 398.0 0.6982 102.0 103.0 158.0 0.6519 0.6456 112.0 112.0 152.0 0.7368 0.7368 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 13.0 117 3.5211 0.0057 2895.5470 2007.0402 400.0 570.0 0.7018 399.0 0.7 102.0 103.0 158.0 0.6519 0.6456 113.0 113.0 152.0 0.7434 0.7434 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 14.0 126 3.5373 0.0057 2908.8753 2016.2787 401.0 570.0 0.7035 400.0 0.7018 103.0 104.0 158.0 0.6582 0.6519 112.0 112.0 152.0 0.7368 0.7368 108.0 108.0 142.0 0.7606 0.7606 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 15.0 135 3.5497 0.0057 2919.0747 2023.3484 400.0 570.0 0.7018 399.0 0.7 103.0 104.0 158.0 0.6582 0.6519 112.0 112.0 152.0 0.7368 0.7368 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 16.0 144 3.5400 0.0057 2911.0814 2017.8079 399.0 570.0 0.7 398.0 0.6982 102.0 103.0 158.0 0.6519 0.6456 112.0 112.0 152.0 0.7368 0.7368 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 17.0 153 3.5550 0.0057 2923.3854 2026.3364 398.0 570.0 0.6982 397.0 0.6965 102.0 103.0 158.0 0.6519 0.6456 111.0 111.0 152.0 0.7303 0.7303 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 18.0 162 3.5753 0.0057 2940.0777 2037.9065 398.0 570.0 0.6982 397.0 0.6965 102.0 103.0 158.0 0.6519 0.6456 111.0 111.0 152.0 0.7303 0.7303 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 19.0 171 3.5759 0.0057 2940.5951 2038.2652 400.0 570.0 0.7018 399.0 0.7 103.0 104.0 158.0 0.6582 0.6519 112.0 112.0 152.0 0.7368 0.7368 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 20.0 180 3.5724 0.0057 2937.7403 2036.2864 401.0 570.0 0.7035 400.0 0.7018 103.0 104.0 158.0 0.6582 0.6519 113.0 113.0 152.0 0.7434 0.7434 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 21.0 189 3.5773 0.0057 2941.7130 2039.0401 401.0 570.0 0.7035 400.0 0.7018 103.0 104.0 158.0 0.6582 0.6519 113.0 113.0 152.0 0.7434 0.7434 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 22.0 198 3.5691 0.0057 2934.9847 2034.3764 400.0 570.0 0.7018 399.0 0.7 102.0 103.0 158.0 0.6519 0.6456 113.0 113.0 152.0 0.7434 0.7434 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 23.0 207 3.5653 0.0057 2931.9084 2032.2441 401.0 570.0 0.7035 400.0 0.7018 103.0 104.0 158.0 0.6582 0.6519 113.0 113.0 152.0 0.7434 0.7434 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 24.0 216 3.5999 0.0057 2960.3382 2051.9501 400.0 570.0 0.7018 399.0 0.7 102.0 103.0 158.0 0.6519 0.6456 113.0 113.0 152.0 0.7434 0.7434 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 25.0 225 3.5823 0.0057 2945.8462 2041.9050 402.0 570.0 0.7053 401.0 0.7035 103.0 104.0 158.0 0.6582 0.6519 113.0 113.0 152.0 0.7434 0.7434 107.0 107.0 142.0 0.7535 0.7535 78.0 78.0 118.0 0.6610 0.6610 0.0 0.0 0.0 0.0 0.0
0.0 26.0 234 3.6104 0.0057 2968.9215 2057.8995 399.0 570.0 0.7 398.0 0.6982 102.0 103.0 158.0 0.6519 0.6456 112.0 112.0 152.0 0.7368 0.7368 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 27.0 243 3.6075 0.0057 2966.5800 2056.2766 399.0 570.0 0.7 398.0 0.6982 102.0 103.0 158.0 0.6519 0.6456 112.0 112.0 152.0 0.7368 0.7368 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 28.0 252 3.6379 0.0057 2991.5786 2073.6043 399.0 570.0 0.7 398.0 0.6982 102.0 103.0 158.0 0.6519 0.6456 112.0 112.0 152.0 0.7368 0.7368 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 29.0 261 3.6116 0.0057 2969.9806 2058.6337 400.0 570.0 0.7018 399.0 0.7 103.0 104.0 158.0 0.6582 0.6519 112.0 112.0 152.0 0.7368 0.7368 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 30.0 270 3.6136 0.0057 2971.5578 2059.7269 399.0 570.0 0.7 398.0 0.6982 102.0 103.0 158.0 0.6519 0.6456 112.0 112.0 152.0 0.7368 0.7368 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 31.0 279 3.6217 0.0057 2978.2495 2064.3652 400.0 570.0 0.7018 399.0 0.7 102.0 103.0 158.0 0.6519 0.6456 113.0 113.0 152.0 0.7434 0.7434 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 32.0 288 3.6315 0.0057 2986.2913 2069.9394 399.0 570.0 0.7 398.0 0.6982 102.0 103.0 158.0 0.6519 0.6456 112.0 112.0 152.0 0.7368 0.7368 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 33.0 297 3.6548 0.0057 3005.4758 2083.2371 399.0 570.0 0.7 398.0 0.6982 102.0 103.0 158.0 0.6519 0.6456 112.0 112.0 152.0 0.7368 0.7368 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 0.0 0.0 0.0 0.0 0.0
0.0 34.0 306 3.6216 0.0057 2978.1381 2064.2880 402.0 570.0 0.7053 401.0 0.7035 103.0 104.0 158.0 0.6582 0.6519 113.0 113.0 152.0 0.7434 0.7434 107.0 107.0 142.0 0.7535 0.7535 78.0 78.0 118.0 0.6610 0.6610 0.0 0.0 0.0 0.0 0.0
0.0 35.0 315 3.6415 0.0057 2994.5376 2075.6553 400.0 570.0 0.7018 399.0 0.7 102.0 103.0 158.0 0.6519 0.6456 112.0 112.0 152.0 0.7368 0.7368 107.0 107.0 142.0 0.7535 0.7535 78.0 78.0 118.0 0.6610 0.6610 0.0 0.0 0.0 0.0 0.0
0.0 36.0 324 3.6455 0.0057 2997.8203 2077.9307 400.0 570.0 0.7018 399.0 0.7 102.0 103.0 158.0 0.6519 0.6456 113.0 113.0 152.0 0.7434 0.7434 107.0 107.0 142.0 0.7535 0.7535 77.0 77.0 118.0 0.6525 0.6525 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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