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---
library_name: peft
base_model: peiyi9979/math-shepherd-mistral-7b-prm
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: v3b_mistral_lora
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# v3b_mistral_lora
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: 0.2975
- Accuracy: 0.8647
- Precision: 0.8701
- Recall: 0.6087
- F1: 0.7163
## 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: 765837
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- 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: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 0 | 0 | 0.6026 | 0.7339 | 0.6 | 0.1542 | 0.2453 |
| 0.5256 | 0.0095 | 20 | 0.6015 | 0.7350 | 0.6061 | 0.1581 | 0.2508 |
| 0.6118 | 0.0189 | 40 | 0.5988 | 0.7361 | 0.6087 | 0.1660 | 0.2609 |
| 0.5575 | 0.0284 | 60 | 0.5849 | 0.7450 | 0.6456 | 0.2016 | 0.3072 |
| 0.6385 | 0.0378 | 80 | 0.5648 | 0.7461 | 0.5938 | 0.3004 | 0.3990 |
| 0.4791 | 0.0473 | 100 | 0.5396 | 0.7661 | 0.6694 | 0.3281 | 0.4403 |
| 0.3593 | 0.0567 | 120 | 0.5030 | 0.7794 | 0.7109 | 0.3597 | 0.4777 |
| 0.4435 | 0.0662 | 140 | 0.4716 | 0.7794 | 0.6467 | 0.4704 | 0.5446 |
| 0.3899 | 0.0757 | 160 | 0.4403 | 0.7938 | 0.7519 | 0.3953 | 0.5181 |
| 0.3429 | 0.0851 | 180 | 0.4055 | 0.8160 | 0.7771 | 0.4822 | 0.5951 |
| 0.3529 | 0.0946 | 200 | 0.3847 | 0.8182 | 0.7405 | 0.5415 | 0.6256 |
| 0.36 | 0.1040 | 220 | 0.3824 | 0.8182 | 0.7697 | 0.5020 | 0.6077 |
| 0.2875 | 0.1135 | 240 | 0.3578 | 0.8226 | 0.7385 | 0.5692 | 0.6429 |
| 0.3237 | 0.1229 | 260 | 0.3457 | 0.8426 | 0.7342 | 0.6877 | 0.7102 |
| 0.2309 | 0.1324 | 280 | 0.3626 | 0.8204 | 0.8527 | 0.4348 | 0.5759 |
| 0.2843 | 0.1418 | 300 | 0.3511 | 0.8326 | 0.8493 | 0.4901 | 0.6216 |
| 0.2694 | 0.1513 | 320 | 0.3487 | 0.8337 | 0.8411 | 0.5020 | 0.6287 |
| 0.3854 | 0.1608 | 340 | 0.3573 | 0.8193 | 0.8358 | 0.4427 | 0.5788 |
| 0.3062 | 0.1702 | 360 | 0.3262 | 0.8470 | 0.7778 | 0.6364 | 0.7 |
| 0.2861 | 0.1797 | 380 | 0.3308 | 0.8459 | 0.8202 | 0.5771 | 0.6775 |
| 0.2808 | 0.1891 | 400 | 0.3584 | 0.8337 | 0.8931 | 0.4625 | 0.6094 |
| 0.2716 | 0.1986 | 420 | 0.3312 | 0.8525 | 0.8614 | 0.5652 | 0.6826 |
| 0.3696 | 0.2080 | 440 | 0.3196 | 0.8548 | 0.8020 | 0.6403 | 0.7121 |
| 0.1911 | 0.2175 | 460 | 0.3436 | 0.8426 | 0.8725 | 0.5138 | 0.6468 |
| 0.2548 | 0.2270 | 480 | 0.3311 | 0.8525 | 0.8704 | 0.5573 | 0.6795 |
| 0.2501 | 0.2364 | 500 | 0.3237 | 0.8481 | 0.8671 | 0.5415 | 0.6667 |
| 0.2936 | 0.2459 | 520 | 0.3496 | 0.8359 | 0.8832 | 0.4783 | 0.6205 |
| 0.2012 | 0.2553 | 540 | 0.3362 | 0.8404 | 0.8516 | 0.5217 | 0.6471 |
| 0.3295 | 0.2648 | 560 | 0.3415 | 0.8492 | 0.8462 | 0.5652 | 0.6777 |
| 0.2859 | 0.2742 | 580 | 0.3370 | 0.8437 | 0.8733 | 0.5178 | 0.6501 |
| 0.2655 | 0.2837 | 600 | 0.3248 | 0.8492 | 0.8343 | 0.5771 | 0.6822 |
| 0.2646 | 0.2931 | 620 | 0.3290 | 0.8481 | 0.8625 | 0.5455 | 0.6683 |
| 0.2706 | 0.3026 | 640 | 0.3193 | 0.8481 | 0.8222 | 0.5850 | 0.6836 |
| 0.2074 | 0.3121 | 660 | 0.3506 | 0.8470 | 0.8912 | 0.5178 | 0.655 |
| 0.2825 | 0.3215 | 680 | 0.3523 | 0.8282 | 0.8828 | 0.4466 | 0.5932 |
| 0.2718 | 0.3310 | 700 | 0.3708 | 0.8271 | 0.9008 | 0.4308 | 0.5829 |
| 0.2172 | 0.3404 | 720 | 0.3735 | 0.8237 | 0.9123 | 0.4111 | 0.5668 |
| 0.1876 | 0.3499 | 740 | 0.3519 | 0.8392 | 0.9154 | 0.4704 | 0.6214 |
| 0.2788 | 0.3593 | 760 | 0.3574 | 0.8348 | 0.8611 | 0.4901 | 0.6247 |
| 0.305 | 0.3688 | 780 | 0.3154 | 0.8581 | 0.8492 | 0.6008 | 0.7037 |
| 0.2726 | 0.3783 | 800 | 0.3149 | 0.8459 | 0.875 | 0.5257 | 0.6568 |
| 0.2819 | 0.3877 | 820 | 0.3015 | 0.8581 | 0.7880 | 0.6759 | 0.7277 |
| 0.2596 | 0.3972 | 840 | 0.3099 | 0.8548 | 0.7629 | 0.6996 | 0.7299 |
| 0.185 | 0.4066 | 860 | 0.3079 | 0.8614 | 0.8299 | 0.6364 | 0.7204 |
| 0.189 | 0.4161 | 880 | 0.3248 | 0.8503 | 0.8882 | 0.5336 | 0.6667 |
| 0.299 | 0.4255 | 900 | 0.3174 | 0.8525 | 0.8614 | 0.5652 | 0.6826 |
| 0.199 | 0.4350 | 920 | 0.3387 | 0.8392 | 0.9030 | 0.4783 | 0.6253 |
| 0.2886 | 0.4444 | 940 | 0.3313 | 0.8381 | 0.8794 | 0.4901 | 0.6294 |
| 0.2641 | 0.4539 | 960 | 0.3095 | 0.8636 | 0.8611 | 0.6126 | 0.7159 |
| 0.2316 | 0.4634 | 980 | 0.3030 | 0.8603 | 0.8256 | 0.6364 | 0.7188 |
| 0.2116 | 0.4728 | 1000 | 0.3230 | 0.8581 | 0.8571 | 0.5929 | 0.7009 |
| 0.2134 | 0.4823 | 1020 | 0.3040 | 0.8625 | 0.8057 | 0.6719 | 0.7328 |
| 0.2139 | 0.4917 | 1040 | 0.3280 | 0.8448 | 0.9007 | 0.5020 | 0.6447 |
| 0.1949 | 0.5012 | 1060 | 0.3116 | 0.8625 | 0.8728 | 0.5968 | 0.7089 |
| 0.2255 | 0.5106 | 1080 | 0.3195 | 0.8592 | 0.8663 | 0.5889 | 0.7012 |
| 0.2452 | 0.5201 | 1100 | 0.3464 | 0.8426 | 0.8936 | 0.4980 | 0.6396 |
| 0.2038 | 0.5296 | 1120 | 0.3167 | 0.8570 | 0.8735 | 0.5731 | 0.6921 |
| 0.2496 | 0.5390 | 1140 | 0.3181 | 0.8592 | 0.8795 | 0.5771 | 0.6969 |
| 0.2864 | 0.5485 | 1160 | 0.3201 | 0.8514 | 0.8790 | 0.5455 | 0.6732 |
| 0.2342 | 0.5579 | 1180 | 0.3140 | 0.8647 | 0.8429 | 0.6364 | 0.7252 |
| 0.1366 | 0.5674 | 1200 | 0.3010 | 0.8681 | 0.8384 | 0.6561 | 0.7361 |
| 0.2301 | 0.5768 | 1220 | 0.3011 | 0.8625 | 0.8564 | 0.6126 | 0.7143 |
| 0.2873 | 0.5863 | 1240 | 0.3049 | 0.8625 | 0.8564 | 0.6126 | 0.7143 |
| 0.2467 | 0.5957 | 1260 | 0.3107 | 0.8625 | 0.8686 | 0.6008 | 0.7103 |
| 0.3175 | 0.6052 | 1280 | 0.3120 | 0.8581 | 0.8788 | 0.5731 | 0.6938 |
| 0.1988 | 0.6147 | 1300 | 0.3020 | 0.8636 | 0.8652 | 0.6087 | 0.7146 |
| 0.2081 | 0.6241 | 1320 | 0.3175 | 0.8559 | 0.8820 | 0.5613 | 0.6860 |
| 0.1784 | 0.6336 | 1340 | 0.2959 | 0.8647 | 0.8227 | 0.6601 | 0.7325 |
| 0.2712 | 0.6430 | 1360 | 0.3133 | 0.8592 | 0.85 | 0.6047 | 0.7067 |
| 0.2463 | 0.6525 | 1380 | 0.3180 | 0.8548 | 0.8427 | 0.5929 | 0.6961 |
| 0.3991 | 0.6619 | 1400 | 0.3167 | 0.8625 | 0.8817 | 0.5889 | 0.7062 |
| 0.154 | 0.6714 | 1420 | 0.3027 | 0.8636 | 0.8652 | 0.6087 | 0.7146 |
| 0.1944 | 0.6809 | 1440 | 0.3172 | 0.8625 | 0.8772 | 0.5929 | 0.7075 |
| 0.2434 | 0.6903 | 1460 | 0.3035 | 0.8692 | 0.8325 | 0.6680 | 0.7412 |
| 0.2346 | 0.6998 | 1480 | 0.3163 | 0.8625 | 0.8728 | 0.5968 | 0.7089 |
| 0.2532 | 0.7092 | 1500 | 0.2938 | 0.8659 | 0.83 | 0.6561 | 0.7329 |
| 0.1815 | 0.7187 | 1520 | 0.3156 | 0.8570 | 0.8924 | 0.5573 | 0.6861 |
| 0.1989 | 0.7281 | 1540 | 0.3187 | 0.8614 | 0.8855 | 0.5810 | 0.7017 |
| 0.1749 | 0.7376 | 1560 | 0.3169 | 0.8647 | 0.8786 | 0.6008 | 0.7136 |
| 0.2141 | 0.7470 | 1580 | 0.3046 | 0.8670 | 0.8556 | 0.6324 | 0.7273 |
| 0.2638 | 0.7565 | 1600 | 0.2976 | 0.8670 | 0.8446 | 0.6443 | 0.7309 |
| 0.2215 | 0.7660 | 1620 | 0.2927 | 0.8647 | 0.8359 | 0.6443 | 0.7277 |
| 0.2587 | 0.7754 | 1640 | 0.3179 | 0.8647 | 0.9018 | 0.5810 | 0.7067 |
| 0.2216 | 0.7849 | 1660 | 0.3046 | 0.8714 | 0.8914 | 0.6166 | 0.7290 |
| 0.2357 | 0.7943 | 1680 | 0.2967 | 0.8670 | 0.8556 | 0.6324 | 0.7273 |
| 0.1972 | 0.8038 | 1700 | 0.3002 | 0.8659 | 0.8626 | 0.6206 | 0.7218 |
| 0.2602 | 0.8132 | 1720 | 0.3001 | 0.8670 | 0.8757 | 0.6126 | 0.7209 |
| 0.1873 | 0.8227 | 1740 | 0.3046 | 0.8647 | 0.8786 | 0.6008 | 0.7136 |
| 0.1663 | 0.8322 | 1760 | 0.2978 | 0.8659 | 0.875 | 0.6087 | 0.7179 |
| 0.363 | 0.8416 | 1780 | 0.2977 | 0.8647 | 0.8743 | 0.6047 | 0.7150 |
| 0.1727 | 0.8511 | 1800 | 0.2989 | 0.8659 | 0.875 | 0.6087 | 0.7179 |
| 0.1995 | 0.8605 | 1820 | 0.3006 | 0.8625 | 0.8728 | 0.5968 | 0.7089 |
| 0.154 | 0.8700 | 1840 | 0.2966 | 0.8681 | 0.8681 | 0.6245 | 0.7264 |
| 0.1821 | 0.8794 | 1860 | 0.2968 | 0.8670 | 0.8674 | 0.6206 | 0.7235 |
| 0.2354 | 0.8889 | 1880 | 0.2952 | 0.8670 | 0.8595 | 0.6285 | 0.7260 |
| 0.3563 | 0.8983 | 1900 | 0.2933 | 0.8670 | 0.8556 | 0.6324 | 0.7273 |
| 0.2716 | 0.9078 | 1920 | 0.2968 | 0.8647 | 0.8619 | 0.6166 | 0.7189 |
| 0.1428 | 0.9173 | 1940 | 0.2970 | 0.8636 | 0.8652 | 0.6087 | 0.7146 |
| 0.2108 | 0.9267 | 1960 | 0.2979 | 0.8659 | 0.8667 | 0.6166 | 0.7206 |
| 0.1501 | 0.9362 | 1980 | 0.2986 | 0.8670 | 0.8715 | 0.6166 | 0.7222 |
| 0.2162 | 0.9456 | 2000 | 0.2984 | 0.8625 | 0.8644 | 0.6047 | 0.7116 |
| 0.3241 | 0.9551 | 2020 | 0.2993 | 0.8647 | 0.8701 | 0.6087 | 0.7163 |
| 0.2289 | 0.9645 | 2040 | 0.2976 | 0.8659 | 0.8708 | 0.6126 | 0.7193 |
| 0.1593 | 0.9740 | 2060 | 0.2984 | 0.8636 | 0.8693 | 0.6047 | 0.7133 |
| 0.2018 | 0.9835 | 2080 | 0.2984 | 0.8647 | 0.8659 | 0.6126 | 0.7176 |
| 0.2018 | 0.9929 | 2100 | 0.2975 | 0.8647 | 0.8701 | 0.6087 | 0.7163 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |