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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: v2b_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. -->
# v2b_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: 0.3066
- Accuracy: 0.8603
- Precision: 0.8713
- Recall: 0.5889
- F1: 0.7028
## 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 | 0.5994 | 0.7384 | 0.6545 | 0.1423 | 0.2338 |
| 0.572 | 0.0186 | 20 | 0.5946 | 0.7395 | 0.6731 | 0.1383 | 0.2295 |
| 0.5829 | 0.0371 | 40 | 0.5678 | 0.7494 | 0.7143 | 0.1779 | 0.2848 |
| 0.4808 | 0.0557 | 60 | 0.5129 | 0.7694 | 0.6744 | 0.3439 | 0.4555 |
| 0.498 | 0.0742 | 80 | 0.4658 | 0.7805 | 0.6708 | 0.4269 | 0.5217 |
| 0.2531 | 0.0928 | 100 | 0.4835 | 0.8016 | 0.7312 | 0.4625 | 0.5666 |
| 0.2925 | 0.1113 | 120 | 0.5003 | 0.8016 | 0.8304 | 0.3676 | 0.5096 |
| 0.1912 | 0.1299 | 140 | 0.4575 | 0.8004 | 0.8411 | 0.3557 | 0.5 |
| 0.1991 | 0.1484 | 160 | 0.4109 | 0.8115 | 0.8374 | 0.4071 | 0.5479 |
| 0.2153 | 0.1670 | 180 | 0.3718 | 0.8337 | 0.8456 | 0.4980 | 0.6269 |
| 0.1638 | 0.1855 | 200 | 0.3657 | 0.8237 | 0.8672 | 0.4387 | 0.5827 |
| 0.2033 | 0.2041 | 220 | 0.3455 | 0.8370 | 0.8354 | 0.5217 | 0.6423 |
| 0.2448 | 0.2226 | 240 | 0.3438 | 0.8381 | 0.8497 | 0.5138 | 0.6404 |
| 0.2337 | 0.2412 | 260 | 0.3705 | 0.8282 | 0.8828 | 0.4466 | 0.5932 |
| 0.1698 | 0.2597 | 280 | 0.3724 | 0.8215 | 0.8710 | 0.4269 | 0.5729 |
| 0.1607 | 0.2783 | 300 | 0.3455 | 0.8293 | 0.8722 | 0.4585 | 0.6010 |
| 0.1671 | 0.2968 | 320 | 0.3371 | 0.8337 | 0.8503 | 0.4941 | 0.625 |
| 0.1809 | 0.3154 | 340 | 0.3406 | 0.8514 | 0.8287 | 0.5929 | 0.6912 |
| 0.1672 | 0.3340 | 360 | 0.3520 | 0.8392 | 0.8699 | 0.5020 | 0.6366 |
| 0.153 | 0.3525 | 380 | 0.3273 | 0.8459 | 0.8562 | 0.5415 | 0.6634 |
| 0.2 | 0.3711 | 400 | 0.3307 | 0.8448 | 0.8599 | 0.5336 | 0.6585 |
| 0.2082 | 0.3896 | 420 | 0.3143 | 0.8603 | 0.8396 | 0.6206 | 0.7136 |
| 0.2051 | 0.4082 | 440 | 0.3139 | 0.8570 | 0.8563 | 0.5889 | 0.6979 |
| 0.0959 | 0.4267 | 460 | 0.3130 | 0.8570 | 0.8523 | 0.5929 | 0.6993 |
| 0.1955 | 0.4453 | 480 | 0.3044 | 0.8592 | 0.8462 | 0.6087 | 0.7080 |
| 0.1904 | 0.4638 | 500 | 0.3389 | 0.8404 | 0.8759 | 0.5020 | 0.6382 |
| 0.1809 | 0.4824 | 520 | 0.3319 | 0.8459 | 0.8701 | 0.5296 | 0.6585 |
| 0.1605 | 0.5009 | 540 | 0.3016 | 0.8614 | 0.8678 | 0.5968 | 0.7073 |
| 0.2123 | 0.5195 | 560 | 0.2983 | 0.8603 | 0.8396 | 0.6206 | 0.7136 |
| 0.2279 | 0.5380 | 580 | 0.3046 | 0.8559 | 0.8361 | 0.6047 | 0.7018 |
| 0.2224 | 0.5566 | 600 | 0.3395 | 0.8381 | 0.8741 | 0.4941 | 0.6313 |
| 0.1655 | 0.5751 | 620 | 0.3388 | 0.8359 | 0.8777 | 0.4822 | 0.6224 |
| 0.1468 | 0.5937 | 640 | 0.3022 | 0.8592 | 0.8424 | 0.6126 | 0.7094 |
| 0.1421 | 0.6122 | 660 | 0.3297 | 0.8437 | 0.8784 | 0.5138 | 0.6484 |
| 0.2483 | 0.6308 | 680 | 0.3060 | 0.8525 | 0.8529 | 0.5731 | 0.6856 |
| 0.1411 | 0.6494 | 700 | 0.3171 | 0.8481 | 0.8537 | 0.5534 | 0.6715 |
| 0.2015 | 0.6679 | 720 | 0.3120 | 0.8525 | 0.8614 | 0.5652 | 0.6826 |
| 0.2216 | 0.6865 | 740 | 0.3030 | 0.8503 | 0.8598 | 0.5573 | 0.6763 |
| 0.1936 | 0.7050 | 760 | 0.3091 | 0.8503 | 0.8598 | 0.5573 | 0.6763 |
| 0.135 | 0.7236 | 780 | 0.3023 | 0.8525 | 0.8529 | 0.5731 | 0.6856 |
| 0.1332 | 0.7421 | 800 | 0.3207 | 0.8437 | 0.8836 | 0.5099 | 0.6466 |
| 0.249 | 0.7607 | 820 | 0.3031 | 0.8592 | 0.8706 | 0.5850 | 0.6998 |
| 0.2033 | 0.7792 | 840 | 0.3076 | 0.8592 | 0.875 | 0.5810 | 0.6983 |
| 0.1418 | 0.7978 | 860 | 0.2998 | 0.8614 | 0.8678 | 0.5968 | 0.7073 |
| 0.1826 | 0.8163 | 880 | 0.3014 | 0.8625 | 0.8728 | 0.5968 | 0.7089 |
| 0.1538 | 0.8349 | 900 | 0.3092 | 0.8614 | 0.8855 | 0.5810 | 0.7017 |
| 0.1762 | 0.8534 | 920 | 0.3011 | 0.8603 | 0.8671 | 0.5929 | 0.7042 |
| 0.1561 | 0.8720 | 940 | 0.2998 | 0.8603 | 0.8671 | 0.5929 | 0.7042 |
| 0.1633 | 0.8905 | 960 | 0.3064 | 0.8570 | 0.8690 | 0.5771 | 0.6936 |
| 0.1452 | 0.9091 | 980 | 0.3034 | 0.8603 | 0.8713 | 0.5889 | 0.7028 |
| 0.086 | 0.9276 | 1000 | 0.3051 | 0.8581 | 0.8698 | 0.5810 | 0.6967 |
| 0.1909 | 0.9462 | 1020 | 0.3055 | 0.8581 | 0.8698 | 0.5810 | 0.6967 |
| 0.2017 | 0.9647 | 1040 | 0.3058 | 0.8581 | 0.8743 | 0.5771 | 0.6952 |
| 0.1828 | 0.9833 | 1060 | 0.3066 | 0.8603 | 0.8713 | 0.5889 | 0.7028 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |