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ARC-Challenge_Llama-3.2-1B-jeegnirm

Text Generation
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Safetensors
llama
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text-generation-inference
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ARC-Challenge_Llama-3.2-1B-jeegnirm

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: 4.1740
  • Model Preparation Time: 0.0056
  • Mdl: 1800.5119
  • Accumulated Loss: 1248.0197
  • Correct Preds: 73.0
  • Total Preds: 299.0
  • Accuracy: 0.2441
  • Correct Gen Preds: 73.0
  • Gen Accuracy: 0.2441
  • 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: 73.0
  • Correct Preds 33: 73.0
  • Total Labels 33: 73.0
  • Accuracy 33: 1.0
  • Gen Accuracy 33: 1.0
  • Correct Gen Preds 34: 0.0
  • Correct Preds 34: 0.0
  • Total Labels 34: 78.0
  • Accuracy 34: 0.0
  • Gen Accuracy 34: 0.0
  • Correct Gen Preds 35: 0.0
  • Correct Preds 35: 0.0
  • Total Labels 35: 83.0
  • Accuracy 35: 0.0
  • Gen Accuracy 35: 0.0
  • 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 0.0056 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
1.6112 1.0 1 1.6389 0.0056 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
1.6112 2.0 2 2.6577 0.0056 1146.4553 794.6622 67.0 299.0 0.2241 5.0 0.0167 2.0 28.0 64.0 0.4375 0.0312 3.0 38.0 73.0 0.5205 0.0411 0.0 0.0 78.0 0.0 0.0 0.0 1.0 83.0 0.0120 0.0 0.0 0.0 1.0 0.0 0.0
0.6501 3.0 3 4.1740 0.0056 1800.5119 1248.0197 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
0.8752 4.0 4 3.5170 0.0056 1517.1209 1051.5880 72.0 299.0 0.2408 66.0 0.2207 0.0 0.0 64.0 0.0 0.0 65.0 71.0 73.0 0.9726 0.8904 0.0 0.0 78.0 0.0 0.0 1.0 1.0 83.0 0.0120 0.0120 0.0 0.0 1.0 0.0 0.0
0.0108 5.0 5 5.5078 0.0056 2375.8687 1646.8267 73.0 299.0 0.2441 67.0 0.2241 0.0 0.0 64.0 0.0 0.0 66.0 72.0 73.0 0.9863 0.9041 0.0 0.0 78.0 0.0 0.0 1.0 1.0 83.0 0.0120 0.0120 0.0 0.0 1.0 0.0 0.0
0.0004 6.0 6 6.7942 0.0056 2930.8057 2031.4797 73.0 299.0 0.2441 69.0 0.2308 0.0 0.0 64.0 0.0 0.0 69.0 73.0 73.0 1.0 0.9452 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
0.0001 7.0 7 7.4741 0.0056 3224.0644 2234.7512 73.0 299.0 0.2441 70.0 0.2341 0.0 0.0 64.0 0.0 0.0 70.0 73.0 73.0 1.0 0.9589 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
0.0 8.0 8 7.9209 0.0056 3416.8166 2368.3568 72.0 299.0 0.2408 69.0 0.2308 0.0 0.0 64.0 0.0 0.0 69.0 72.0 73.0 0.9863 0.9452 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
0.0 9.0 9 8.2159 0.0056 3544.0580 2456.5538 72.0 299.0 0.2408 69.0 0.2308 0.0 0.0 64.0 0.0 0.0 69.0 72.0 73.0 0.9863 0.9452 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
0.0 10.0 10 8.4230 0.0056 3633.3945 2518.4772 72.0 299.0 0.2408 69.0 0.2308 0.0 0.0 64.0 0.0 0.0 69.0 72.0 73.0 0.9863 0.9452 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
0.0 11.0 11 8.5792 0.0056 3700.7751 2565.1818 72.0 299.0 0.2408 69.0 0.2308 0.0 0.0 64.0 0.0 0.0 69.0 72.0 73.0 0.9863 0.9452 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
0.0 12.0 12 8.6998 0.0056 3752.7854 2601.2326 72.0 299.0 0.2408 69.0 0.2308 0.0 0.0 64.0 0.0 0.0 69.0 72.0 73.0 0.9863 0.9452 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
0.0 13.0 13 8.7996 0.0056 3795.8541 2631.0856 72.0 299.0 0.2408 68.0 0.2274 0.0 0.0 64.0 0.0 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 14.0 14 8.8839 0.0056 3832.1953 2656.2754 73.0 299.0 0.2441 69.0 0.2308 0.0 1.0 64.0 0.0156 0.0 69.0 72.0 73.0 0.9863 0.9452 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
0.0 15.0 15 8.9564 0.0056 3863.5004 2677.9744 72.0 299.0 0.2408 68.0 0.2274 0.0 0.0 64.0 0.0 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 16.0 16 9.0167 0.0056 3889.4998 2695.9958 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 17.0 17 9.0633 0.0056 3909.6136 2709.9376 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 18.0 18 9.1024 0.0056 3926.4705 2721.6219 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 19.0 19 9.1340 0.0056 3940.0800 2731.0554 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 20.0 20 9.1662 0.0056 3953.9683 2740.6819 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 21.0 21 9.1797 0.0056 3959.8057 2744.7281 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 22.0 22 9.1905 0.0056 3964.4881 2747.9738 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 23.0 23 9.2127 0.0056 3974.0531 2754.6037 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 24.0 24 9.2275 0.0056 3980.4255 2759.0207 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 25.0 25 9.2345 0.0056 3983.4380 2761.1088 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 26.0 26 9.2472 0.0056 3988.9396 2764.9222 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 27.0 27 9.2510 0.0056 3990.5717 2766.0535 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 28.0 28 9.2577 0.0056 3993.4571 2768.0535 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 29.0 29 9.2680 0.0056 3997.9100 2771.1400 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 30.0 30 9.2658 0.0056 3996.9598 2770.4814 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 31.0 31 9.2687 0.0056 3998.1872 2771.3322 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 32.0 32 9.2685 0.0056 3998.0945 2771.2679 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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
0.0 33.0 33 9.2818 0.0056 4003.8405 2775.2507 73.0 299.0 0.2441 68.0 0.2274 0.0 1.0 64.0 0.0156 0.0 68.0 72.0 73.0 0.9863 0.9315 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

Framework versions

  • Transformers 4.51.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.5.0
  • Tokenizers 0.21.1
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meta-llama/Llama-3.2-1B
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