--- base_model: peiyi9979/math-shepherd-mistral-7b-prm library_name: peft metrics: - accuracy - precision - recall - f1 tags: - generated_from_trainer model-index: - name: v1_mistral_lora_real results: [] --- # v1_mistral_lora_real This model is a fine-tuned version of [peiyi9979/math-shepherd-mistral-7b-prm](https://huggingface.co/peiyi9979/math-shepherd-mistral-7b-prm) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 - Accuracy: 1.0 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 ## 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: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Use 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: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.7125 | 0.0071 | 10 | 0.5957 | 0.6805 | 0.5472 | 0.6915 | 0.6110 | | 0.7473 | 0.0143 | 20 | 0.5921 | 0.6931 | 0.5622 | 0.6965 | 0.6222 | | 0.6843 | 0.0214 | 30 | 0.5800 | 0.7094 | 0.5855 | 0.6816 | 0.6299 | | 0.7083 | 0.0285 | 40 | 0.5597 | 0.7401 | 0.6432 | 0.6368 | 0.64 | | 0.6862 | 0.0357 | 50 | 0.5293 | 0.7780 | 0.7216 | 0.6318 | 0.6737 | | 0.626 | 0.0428 | 60 | 0.4788 | 0.8267 | 0.8107 | 0.6816 | 0.7405 | | 0.4406 | 0.0499 | 70 | 0.4027 | 0.8917 | 0.8653 | 0.8308 | 0.8477 | | 0.46 | 0.0571 | 80 | 0.2929 | 0.9386 | 0.9154 | 0.9154 | 0.9154 | | 0.3254 | 0.0642 | 90 | 0.1629 | 0.9819 | 0.9848 | 0.9652 | 0.9749 | | 0.2359 | 0.0714 | 100 | 0.0554 | 0.9982 | 0.9950 | 1.0 | 0.9975 | | 0.263 | 0.0785 | 110 | 0.0200 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.228 | 0.0856 | 120 | 0.0094 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.2553 | 0.0928 | 130 | 0.0114 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1633 | 0.0999 | 140 | 0.0083 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.145 | 0.1070 | 150 | 0.0087 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1409 | 0.1142 | 160 | 0.0041 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1955 | 0.1213 | 170 | 0.0042 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1628 | 0.1284 | 180 | 0.0036 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1454 | 0.1356 | 190 | 0.0019 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1311 | 0.1427 | 200 | 0.0044 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1937 | 0.1498 | 210 | 0.0035 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1059 | 0.1570 | 220 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1352 | 0.1641 | 230 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1491 | 0.1712 | 240 | 0.0019 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1245 | 0.1784 | 250 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1354 | 0.1855 | 260 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1177 | 0.1927 | 270 | 0.0012 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1424 | 0.1998 | 280 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1343 | 0.2069 | 290 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1567 | 0.2141 | 300 | 0.0010 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1094 | 0.2212 | 310 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1537 | 0.2283 | 320 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1344 | 0.2355 | 330 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1286 | 0.2426 | 340 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.142 | 0.2497 | 350 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1177 | 0.2569 | 360 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1383 | 0.2640 | 370 | 0.0009 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1647 | 0.2711 | 380 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0803 | 0.2783 | 390 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1476 | 0.2854 | 400 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1003 | 0.2925 | 410 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1122 | 0.2997 | 420 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1867 | 0.3068 | 430 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1216 | 0.3139 | 440 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1288 | 0.3211 | 450 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1243 | 0.3282 | 460 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1127 | 0.3354 | 470 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0775 | 0.3425 | 480 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1246 | 0.3496 | 490 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0864 | 0.3568 | 500 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1241 | 0.3639 | 510 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.109 | 0.3710 | 520 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1117 | 0.3782 | 530 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1137 | 0.3853 | 540 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1193 | 0.3924 | 550 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1209 | 0.3996 | 560 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0934 | 0.4067 | 570 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1276 | 0.4138 | 580 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0851 | 0.4210 | 590 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1056 | 0.4281 | 600 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0951 | 0.4352 | 610 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1308 | 0.4424 | 620 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0814 | 0.4495 | 630 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0696 | 0.4567 | 640 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0721 | 0.4638 | 650 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0962 | 0.4709 | 660 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0829 | 0.4781 | 670 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1158 | 0.4852 | 680 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0949 | 0.4923 | 690 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1287 | 0.4995 | 700 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0834 | 0.5066 | 710 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.099 | 0.5137 | 720 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.12 | 0.5209 | 730 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0571 | 0.5280 | 740 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1133 | 0.5351 | 750 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1178 | 0.5423 | 760 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0866 | 0.5494 | 770 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0964 | 0.5565 | 780 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1165 | 0.5637 | 790 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1174 | 0.5708 | 800 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1468 | 0.5780 | 810 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1128 | 0.5851 | 820 | 0.0004 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1446 | 0.5922 | 830 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0961 | 0.5994 | 840 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0736 | 0.6065 | 850 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0847 | 0.6136 | 860 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.139 | 0.6208 | 870 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0775 | 0.6279 | 880 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0916 | 0.6350 | 890 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0944 | 0.6422 | 900 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1242 | 0.6493 | 910 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0975 | 0.6564 | 920 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0896 | 0.6636 | 930 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1359 | 0.6707 | 940 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0905 | 0.6778 | 950 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1045 | 0.6850 | 960 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0806 | 0.6921 | 970 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1121 | 0.6993 | 980 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1184 | 0.7064 | 990 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0945 | 0.7135 | 1000 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1041 | 0.7207 | 1010 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0912 | 0.7278 | 1020 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1167 | 0.7349 | 1030 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0952 | 0.7421 | 1040 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1048 | 0.7492 | 1050 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0877 | 0.7563 | 1060 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1051 | 0.7635 | 1070 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1027 | 0.7706 | 1080 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0802 | 0.7777 | 1090 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1118 | 0.7849 | 1100 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.109 | 0.7920 | 1110 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.097 | 0.7991 | 1120 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1045 | 0.8063 | 1130 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0872 | 0.8134 | 1140 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1075 | 0.8205 | 1150 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1322 | 0.8277 | 1160 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1056 | 0.8348 | 1170 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0884 | 0.8420 | 1180 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1284 | 0.8491 | 1190 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1099 | 0.8562 | 1200 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1023 | 0.8634 | 1210 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.086 | 0.8705 | 1220 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0877 | 0.8776 | 1230 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1032 | 0.8848 | 1240 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1446 | 0.8919 | 1250 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1079 | 0.8990 | 1260 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0716 | 0.9062 | 1270 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1181 | 0.9133 | 1280 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1087 | 0.9204 | 1290 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.086 | 0.9276 | 1300 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.071 | 0.9347 | 1310 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0858 | 0.9418 | 1320 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0859 | 0.9490 | 1330 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1165 | 0.9561 | 1340 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1189 | 0.9633 | 1350 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.142 | 0.9704 | 1360 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1336 | 0.9775 | 1370 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.1183 | 0.9847 | 1380 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0961 | 0.9918 | 1390 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.076 | 0.9989 | 1400 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - PEFT 0.12.0 - Transformers 4.46.0 - Pytorch 2.4.0+cu118 - Datasets 3.0.0 - Tokenizers 0.20.1