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mtzig
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v4_mistral_lora

PEFT
Safetensors
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  1. README.md +44 -44
  2. adapter_model.safetensors +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  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.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2886
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- - Accuracy: 0.8631
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- - Precision: 0.8457
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- - Recall: 0.6260
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- - F1: 0.7195
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  ## Model description
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@@ -62,45 +62,45 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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  | No log | 0 | 0 | 0.5995 | 0.7340 | 0.6 | 0.1535 | 0.2445 |
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- | 0.7266 | 0.0251 | 20 | 0.5909 | 0.7384 | 0.6232 | 0.1693 | 0.2663 |
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- | 0.674 | 0.0502 | 40 | 0.5497 | 0.7517 | 0.6526 | 0.2441 | 0.3553 |
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- | 0.5187 | 0.0753 | 60 | 0.4896 | 0.7759 | 0.6409 | 0.4567 | 0.5333 |
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- | 0.4811 | 0.1004 | 80 | 0.4410 | 0.7958 | 0.7066 | 0.4646 | 0.5606 |
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- | 0.2811 | 0.1255 | 100 | 0.4249 | 0.8102 | 0.7595 | 0.4724 | 0.5825 |
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- | 0.2959 | 0.1506 | 120 | 0.3751 | 0.8212 | 0.7674 | 0.5197 | 0.6197 |
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- | 0.336 | 0.1757 | 140 | 0.3764 | 0.8278 | 0.8451 | 0.4724 | 0.6061 |
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- | 0.3239 | 0.2008 | 160 | 0.3608 | 0.8201 | 0.8421 | 0.4409 | 0.5788 |
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- | 0.2767 | 0.2259 | 180 | 0.3362 | 0.8543 | 0.8112 | 0.6260 | 0.7067 |
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- | 0.276 | 0.2510 | 200 | 0.3406 | 0.8389 | 0.8462 | 0.5197 | 0.6439 |
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- | 0.2715 | 0.2762 | 220 | 0.3223 | 0.8411 | 0.8274 | 0.5472 | 0.6588 |
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- | 0.2737 | 0.3013 | 240 | 0.3202 | 0.8521 | 0.8125 | 0.6142 | 0.6996 |
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- | 0.3245 | 0.3264 | 260 | 0.3098 | 0.8466 | 0.8249 | 0.5748 | 0.6775 |
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- | 0.2868 | 0.3515 | 280 | 0.3159 | 0.8433 | 0.7887 | 0.6024 | 0.6830 |
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- | 0.2601 | 0.3766 | 300 | 0.3105 | 0.8587 | 0.7669 | 0.7126 | 0.7388 |
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- | 0.2597 | 0.4017 | 320 | 0.3162 | 0.8510 | 0.8362 | 0.5827 | 0.6868 |
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- | 0.287 | 0.4268 | 340 | 0.2997 | 0.8532 | 0.8071 | 0.6260 | 0.7051 |
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- | 0.3115 | 0.4519 | 360 | 0.3028 | 0.8543 | 0.8315 | 0.6024 | 0.6986 |
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- | 0.2654 | 0.4770 | 380 | 0.3008 | 0.8543 | 0.8245 | 0.6102 | 0.7014 |
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- | 0.2443 | 0.5021 | 400 | 0.2955 | 0.8565 | 0.8039 | 0.6457 | 0.7162 |
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- | 0.2743 | 0.5272 | 420 | 0.3011 | 0.8543 | 0.8389 | 0.5945 | 0.6959 |
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- | 0.2248 | 0.5523 | 440 | 0.3031 | 0.8532 | 0.8380 | 0.5906 | 0.6928 |
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- | 0.2149 | 0.5774 | 460 | 0.2868 | 0.8609 | 0.7991 | 0.6732 | 0.7308 |
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- | 0.1998 | 0.6025 | 480 | 0.2975 | 0.8587 | 0.8316 | 0.6220 | 0.7117 |
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- | 0.2459 | 0.6276 | 500 | 0.2978 | 0.8510 | 0.8324 | 0.5866 | 0.6882 |
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- | 0.1953 | 0.6527 | 520 | 0.2989 | 0.8576 | 0.8492 | 0.5984 | 0.7021 |
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- | 0.3153 | 0.6778 | 540 | 0.2864 | 0.8642 | 0.8359 | 0.6417 | 0.7261 |
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- | 0.2172 | 0.7029 | 560 | 0.3190 | 0.8444 | 0.8844 | 0.5118 | 0.6484 |
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- | 0.2604 | 0.7280 | 580 | 0.2830 | 0.8687 | 0.8358 | 0.6614 | 0.7385 |
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- | 0.2671 | 0.7531 | 600 | 0.2970 | 0.8565 | 0.8523 | 0.5906 | 0.6977 |
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- | 0.2049 | 0.7782 | 620 | 0.2862 | 0.8587 | 0.8316 | 0.6220 | 0.7117 |
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- | 0.2972 | 0.8033 | 640 | 0.2890 | 0.8609 | 0.8404 | 0.6220 | 0.7149 |
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- | 0.1953 | 0.8285 | 660 | 0.2911 | 0.8609 | 0.8441 | 0.6181 | 0.7136 |
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- | 0.24 | 0.8536 | 680 | 0.2824 | 0.8653 | 0.8367 | 0.6457 | 0.7289 |
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- | 0.282 | 0.8787 | 700 | 0.2860 | 0.8631 | 0.8385 | 0.6339 | 0.7220 |
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- | 0.1931 | 0.9038 | 720 | 0.2885 | 0.8620 | 0.8413 | 0.6260 | 0.7178 |
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- | 0.2251 | 0.9289 | 740 | 0.2898 | 0.8631 | 0.8457 | 0.6260 | 0.7195 |
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- | 0.178 | 0.9540 | 760 | 0.2889 | 0.8631 | 0.8457 | 0.6260 | 0.7195 |
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- | 0.2431 | 0.9791 | 780 | 0.2886 | 0.8631 | 0.8457 | 0.6260 | 0.7195 |
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  ### Framework versions
 
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  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.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2841
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+ - Accuracy: 0.8687
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+ - Precision: 0.8392
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+ - Recall: 0.6575
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+ - F1: 0.7373
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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  | No log | 0 | 0 | 0.5995 | 0.7340 | 0.6 | 0.1535 | 0.2445 |
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+ | 0.6057 | 0.0254 | 20 | 0.5918 | 0.7373 | 0.625 | 0.1575 | 0.2516 |
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+ | 0.5356 | 0.0507 | 40 | 0.5521 | 0.7517 | 0.6790 | 0.2165 | 0.3284 |
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+ | 0.5141 | 0.0761 | 60 | 0.5021 | 0.7627 | 0.5890 | 0.5079 | 0.5455 |
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+ | 0.3594 | 0.1015 | 80 | 0.4427 | 0.7980 | 0.7448 | 0.4252 | 0.5414 |
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+ | 0.3988 | 0.1268 | 100 | 0.4067 | 0.8245 | 0.7684 | 0.5354 | 0.6311 |
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+ | 0.3205 | 0.1522 | 120 | 0.3738 | 0.8201 | 0.8138 | 0.4646 | 0.5915 |
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+ | 0.3026 | 0.1776 | 140 | 0.3680 | 0.8289 | 0.8235 | 0.4961 | 0.6192 |
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+ | 0.2886 | 0.2030 | 160 | 0.3467 | 0.8433 | 0.8590 | 0.5276 | 0.6537 |
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+ | 0.2345 | 0.2283 | 180 | 0.3289 | 0.8587 | 0.7972 | 0.6654 | 0.7253 |
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+ | 0.2964 | 0.2537 | 200 | 0.3322 | 0.8377 | 0.8497 | 0.5118 | 0.6388 |
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+ | 0.2655 | 0.2791 | 220 | 0.3495 | 0.8278 | 0.8657 | 0.4567 | 0.5979 |
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+ | 0.3252 | 0.3044 | 240 | 0.3189 | 0.8455 | 0.8314 | 0.5630 | 0.6714 |
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+ | 0.2561 | 0.3298 | 260 | 0.3228 | 0.8532 | 0.8201 | 0.6102 | 0.6998 |
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+ | 0.1661 | 0.3552 | 280 | 0.3141 | 0.8499 | 0.8598 | 0.5551 | 0.6746 |
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+ | 0.1812 | 0.3805 | 300 | 0.3330 | 0.8300 | 0.8378 | 0.4882 | 0.6169 |
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+ | 0.3265 | 0.4059 | 320 | 0.2961 | 0.8543 | 0.8280 | 0.6063 | 0.7 |
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+ | 0.2217 | 0.4313 | 340 | 0.2970 | 0.8664 | 0.8065 | 0.6890 | 0.7431 |
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+ | 0.2058 | 0.4567 | 360 | 0.3054 | 0.8521 | 0.8333 | 0.5906 | 0.6912 |
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+ | 0.225 | 0.4820 | 380 | 0.3018 | 0.8576 | 0.8531 | 0.5945 | 0.7007 |
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+ | 0.2045 | 0.5074 | 400 | 0.3174 | 0.8510 | 0.8742 | 0.5472 | 0.6731 |
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+ | 0.2368 | 0.5328 | 420 | 0.3156 | 0.8477 | 0.8537 | 0.5512 | 0.6699 |
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+ | 0.2162 | 0.5581 | 440 | 0.2928 | 0.8609 | 0.8441 | 0.6181 | 0.7136 |
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+ | 0.1664 | 0.5835 | 460 | 0.2978 | 0.8598 | 0.8325 | 0.6260 | 0.7146 |
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+ | 0.2282 | 0.6089 | 480 | 0.3031 | 0.8587 | 0.8539 | 0.5984 | 0.7037 |
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+ | 0.1983 | 0.6342 | 500 | 0.2958 | 0.8543 | 0.8177 | 0.6181 | 0.7040 |
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+ | 0.1843 | 0.6596 | 520 | 0.3055 | 0.8609 | 0.8556 | 0.6063 | 0.7097 |
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+ | 0.1915 | 0.6850 | 540 | 0.2818 | 0.8675 | 0.8160 | 0.6811 | 0.7425 |
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+ | 0.1582 | 0.7104 | 560 | 0.2887 | 0.8675 | 0.8641 | 0.6260 | 0.7260 |
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+ | 0.2003 | 0.7357 | 580 | 0.2872 | 0.8653 | 0.8511 | 0.6299 | 0.7240 |
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+ | 0.2345 | 0.7611 | 600 | 0.2827 | 0.8687 | 0.8293 | 0.6693 | 0.7407 |
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+ | 0.2107 | 0.7865 | 620 | 0.2954 | 0.8642 | 0.8701 | 0.6063 | 0.7146 |
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+ | 0.2562 | 0.8118 | 640 | 0.2938 | 0.8642 | 0.8503 | 0.6260 | 0.7211 |
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+ | 0.1054 | 0.8372 | 660 | 0.2917 | 0.8642 | 0.8503 | 0.6260 | 0.7211 |
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+ | 0.2837 | 0.8626 | 680 | 0.2842 | 0.8664 | 0.8376 | 0.6496 | 0.7317 |
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+ | 0.1779 | 0.8879 | 700 | 0.2841 | 0.8709 | 0.8477 | 0.6575 | 0.7406 |
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+ | 0.2277 | 0.9133 | 720 | 0.2847 | 0.8675 | 0.8384 | 0.6535 | 0.7345 |
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+ | 0.2099 | 0.9387 | 740 | 0.2828 | 0.8720 | 0.8485 | 0.6614 | 0.7434 |
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+ | 0.2167 | 0.9641 | 760 | 0.2835 | 0.8709 | 0.8477 | 0.6575 | 0.7406 |
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+ | 0.1901 | 0.9894 | 780 | 0.2841 | 0.8687 | 0.8392 | 0.6575 | 0.7373 |
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  ### Framework versions
adapter_model.safetensors CHANGED
@@ -1,3 +1,3 @@
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