--- library_name: transformers base_model: PowerInfer/SmallThinker-3B-Preview tags: - generated_from_trainer model-index: - name: smartmind-cyberone-20250410_x10 results: [] --- # smartmind-cyberone-20250410_x10 This model is a fine-tuned version of [PowerInfer/SmallThinker-3B-Preview](https://huggingface.co/PowerInfer/SmallThinker-3B-Preview) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0078 ## 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - 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_with_restarts - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:-----:|:---------------:| | 0.5867 | 0.0499 | 310 | 0.1835 | | 0.2091 | 0.0998 | 620 | 0.1088 | | 0.1618 | 0.1497 | 930 | 0.0802 | | 0.1325 | 0.1996 | 1240 | 0.0467 | | 0.1496 | 0.2495 | 1550 | 0.0908 | | 0.1206 | 0.2994 | 1860 | 0.0129 | | 0.0787 | 0.3493 | 2170 | 0.0497 | | 0.1031 | 0.3992 | 2480 | 0.0679 | | 0.1326 | 0.4491 | 2790 | 0.1064 | | 0.0932 | 0.4990 | 3100 | 0.0284 | | 0.0869 | 0.5488 | 3410 | 0.0149 | | 0.0765 | 0.5987 | 3720 | 0.0170 | | 0.074 | 0.6486 | 4030 | 0.0338 | | 0.073 | 0.6985 | 4340 | 0.0443 | | 0.0862 | 0.7484 | 4650 | 0.0349 | | 0.0961 | 0.7983 | 4960 | 0.0203 | | 0.1037 | 0.8482 | 5270 | 0.0373 | | 0.0705 | 0.8981 | 5580 | 0.0240 | | 0.0695 | 0.9480 | 5890 | 0.0704 | | 0.0686 | 0.9979 | 6200 | 0.0189 | | 0.061 | 1.0478 | 6510 | 0.0178 | | 0.0562 | 1.0977 | 6820 | 0.0262 | | 0.0707 | 1.1476 | 7130 | 0.0189 | | 0.0538 | 1.1975 | 7440 | 0.0137 | | 0.0498 | 1.2474 | 7750 | 0.0146 | | 0.0419 | 1.2973 | 8060 | 0.0193 | | 0.0373 | 1.3472 | 8370 | 0.0120 | | 0.0305 | 1.3971 | 8680 | 0.0126 | | 0.0276 | 1.4470 | 8990 | 0.0098 | | 0.0257 | 1.4969 | 9300 | 0.0125 | | 0.0288 | 1.5468 | 9610 | 0.0128 | | 0.0281 | 1.5967 | 9920 | 0.0072 | | 0.0273 | 1.6465 | 10230 | 0.0085 | | 0.0238 | 1.6964 | 10540 | 0.0157 | | 0.0237 | 1.7463 | 10850 | 0.0088 | | 0.0227 | 1.7962 | 11160 | 0.0125 | | 0.0237 | 1.8461 | 11470 | 0.0107 | | 0.0244 | 1.8960 | 11780 | 0.0063 | | 0.0201 | 1.9459 | 12090 | 0.0047 | | 0.023 | 1.9958 | 12400 | 0.0049 | | 0.0211 | 2.0457 | 12710 | 0.0038 | | 0.0171 | 2.0956 | 13020 | 0.0057 | | 0.0229 | 2.1455 | 13330 | 0.0097 | | 0.018 | 2.1954 | 13640 | 0.0060 | | 0.0162 | 2.2453 | 13950 | 0.0089 | | 0.0202 | 2.2952 | 14260 | 0.0098 | | 0.0171 | 2.3451 | 14570 | 0.0072 | | 0.0195 | 2.3950 | 14880 | 0.0044 | | 0.0195 | 2.4449 | 15190 | 0.0043 | | 0.0173 | 2.4948 | 15500 | 0.0046 | | 0.015 | 2.5447 | 15810 | 0.0039 | | 0.0149 | 2.5946 | 16120 | 0.0041 | | 0.0204 | 2.6445 | 16430 | 0.0041 | | 0.0173 | 2.6944 | 16740 | 0.0041 | | 0.0181 | 2.7442 | 17050 | 0.0041 | | 0.0165 | 2.7941 | 17360 | 0.0067 | | 0.0326 | 2.8440 | 17670 | 0.0464 | | 0.0732 | 2.8939 | 17980 | 0.0393 | | 0.0367 | 2.9438 | 18290 | 0.0190 | | 0.0515 | 2.9937 | 18600 | 0.0347 | | 0.0348 | 3.0436 | 18910 | 0.0107 | | 0.0288 | 3.0935 | 19220 | 0.0103 | | 0.0363 | 3.1434 | 19530 | 0.0140 | | 0.0409 | 3.1933 | 19840 | 0.0131 | | 0.0211 | 3.2432 | 20150 | 0.0091 | | 0.0279 | 3.2931 | 20460 | 0.0164 | | 0.0286 | 3.3430 | 20770 | 0.0212 | | 0.0244 | 3.3929 | 21080 | 0.0140 | | 0.0301 | 3.4428 | 21390 | 0.0317 | | 0.0274 | 3.4927 | 21700 | 0.0140 | | 0.0245 | 3.5426 | 22010 | 0.0175 | | 0.0216 | 3.5925 | 22320 | 0.0160 | | 0.0209 | 3.6424 | 22630 | 0.0150 | | 0.0243 | 3.6923 | 22940 | 0.0137 | | 0.0255 | 3.7422 | 23250 | 0.0192 | | 0.0233 | 3.7920 | 23560 | 0.0168 | | 0.021 | 3.8419 | 23870 | 0.0210 | | 0.021 | 3.8918 | 24180 | 0.0104 | | 0.0174 | 3.9417 | 24490 | 0.0121 | | 0.0195 | 3.9916 | 24800 | 0.0090 | | 0.0168 | 4.0415 | 25110 | 0.0100 | | 0.0198 | 4.0914 | 25420 | 0.0093 | | 0.0208 | 4.1413 | 25730 | 0.0103 | | 0.0197 | 4.1912 | 26040 | 0.0103 | | 0.0204 | 4.2411 | 26350 | 0.0097 | | 0.0156 | 4.2910 | 26660 | 0.0101 | | 0.0163 | 4.3409 | 26970 | 0.0120 | | 0.0168 | 4.3908 | 27280 | 0.0104 | | 0.0192 | 4.4407 | 27590 | 0.0095 | | 0.0175 | 4.4906 | 27900 | 0.0089 | | 0.0185 | 4.5405 | 28210 | 0.0089 | | 0.0163 | 4.5904 | 28520 | 0.0077 | | 0.0135 | 4.6403 | 28830 | 0.0074 | | 0.0136 | 4.6902 | 29140 | 0.0078 | | 0.0138 | 4.7401 | 29450 | 0.0077 | | 0.016 | 4.7900 | 29760 | 0.0076 | | 0.0136 | 4.8399 | 30070 | 0.0078 | | 0.0199 | 4.8897 | 30380 | 0.0078 | | 0.0155 | 4.9396 | 30690 | 0.0078 | | 0.0136 | 4.9895 | 31000 | 0.0078 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1