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arithmetic-circuit-overloading/Llama-3.3-70B-Instruct-3d-1M-100K-0.2-reverse-padzero-plus-mul-sub-99-256D-2L-8H-1024I

sourceHugging Facellama3.3updated 7mo agoView on Hugging Face
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Model Card

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Llama-3.3-70B-Instruct-3d-1M-100K-0.2-reverse-padzero-plus-mul-sub-99-256D-2L-8H-1024I

This model is a fine-tuned version of meta-llama/Llama-3.3-70B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

  • —Loss: 1.0463

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: 0.001
  • —trainbatchsize: 128
  • —evalbatchsize: 128
  • —seed: 42
  • —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
  • —lrschedulertype: cosine
  • —lrschedulerwarmup_ratio: 0.05
  • —num_epochs: 5

Training results

Training LossEpochStepValidation Loss
No log003.0675
1.71350.06405001.6910
1.41680.128010001.4069
1.37360.192015001.3688
1.21990.256020001.2212
1.19140.320025001.1890
1.17940.384030001.1733
1.16110.448035001.1600
1.15630.512040001.1560
1.15280.576045001.1530
1.15010.640050001.1495
1.14720.704055001.1463
1.14150.768060001.1410
1.13690.831965001.1359
1.13050.895970001.1335
1.1270.959975001.1254
1.12111.023980001.1205
1.11781.087985001.1153
1.11031.151990001.1128
1.10691.215995001.1035
1.10081.2799100001.1057
1.09511.3439105001.0929
1.09141.4079110001.0917
1.08831.4719115001.0848
1.08481.5359120001.0882
1.08911.5999125001.0871
1.07871.6639130001.0783
1.07271.7279135001.0760
1.06221.7919140001.0580
1.05861.8559145001.0551
1.04971.9199150001.0505
1.05181.9839155001.0499
1.04962.0479160001.0492
1.04722.1119165001.0484
1.04842.1759170001.0484
1.04782.2399175001.0479
1.04642.3039180001.0480
1.04662.3678185001.0476
1.04612.4318190001.0472
1.04782.4958195001.0472
1.04672.5598200001.0470
1.04772.6238205001.0468
1.0472.6878210001.0469
1.0482.7518215001.0467
1.0462.8158220001.0467
1.0462.8798225001.0466
1.04592.9438230001.0465
1.04743.0078235001.0465
1.04523.0718240001.0465
1.04743.1358245001.0464
1.04443.1998250001.0464
1.04593.2638255001.0464
1.04633.3278260001.0463
1.04653.3918265001.0464
1.04543.4558270001.0463
1.04523.5198275001.0463
1.04613.5838280001.0463
1.04653.6478285001.0463
1.04593.7118290001.0463
1.04653.7758295001.0463
1.04663.8398300001.0463
1.04513.9038305001.0463
1.04633.9677310001.0463
1.0454.0317315001.0463
1.04584.0957320001.0463
1.04714.1597325001.0463
1.04664.2237330001.0463
1.04574.2877335001.0462
1.04594.3517340001.0463
1.04624.4157345001.0463
1.04494.4797350001.0463
1.0464.5437355001.0463
1.04584.6077360001.0463
1.04634.6717365001.0463
1.04714.7357370001.0463
1.04624.7997375001.0463
1.04584.8637380001.0463
1.04574.9277385001.0463
1.04644.9917390001.0463

Framework versions

  • —Transformers 4.57.1
  • —Pytorch 2.9.0+cu128
  • —Datasets 4.5.0
  • —Tokenizers 0.22.1