File size: 5,401 Bytes
e3b9cab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
---
library_name: peft
base_model: peiyi9979/math-shepherd-mistral-7b-prm
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: v3_mistral_balance1_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. -->
# v3_mistral_balance1_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: 0.0130
- Accuracy: 0.9980
- Precision: 0.9818
- Recall: 0.9474
- F1: 0.9643
## 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: 8569382
- 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 | 0.3256 | 0.9369 | 0.1429 | 0.2456 | 0.1806 |
| 0.4529 | 0.0258 | 20 | 0.2883 | 0.9484 | 0.1493 | 0.1754 | 0.1613 |
| 0.2483 | 0.0515 | 40 | 0.1461 | 0.9672 | 0.2 | 0.0526 | 0.0833 |
| 0.1622 | 0.0773 | 60 | 0.1080 | 0.9687 | 0.35 | 0.1228 | 0.1818 |
| 0.1243 | 0.1031 | 80 | 0.0879 | 0.9697 | 0.4524 | 0.3333 | 0.3838 |
| 0.0678 | 0.1289 | 100 | 0.0700 | 0.9692 | 0.4719 | 0.7368 | 0.5753 |
| 0.0301 | 0.1546 | 120 | 0.0474 | 0.9836 | 0.65 | 0.9123 | 0.7591 |
| 0.0105 | 0.1804 | 140 | 0.0342 | 0.9911 | 0.8421 | 0.8421 | 0.8421 |
| 0.041 | 0.2062 | 160 | 0.0333 | 0.9926 | 0.875 | 0.8596 | 0.8673 |
| 0.0291 | 0.2320 | 180 | 0.0268 | 0.9930 | 0.8308 | 0.9474 | 0.8852 |
| 0.0366 | 0.2577 | 200 | 0.0262 | 0.9916 | 0.7941 | 0.9474 | 0.864 |
| 0.0133 | 0.2835 | 220 | 0.0206 | 0.9921 | 0.8154 | 0.9298 | 0.8689 |
| 0.0075 | 0.3093 | 240 | 0.0188 | 0.9955 | 0.9444 | 0.8947 | 0.9189 |
| 0.0036 | 0.3351 | 260 | 0.0168 | 0.9945 | 0.9107 | 0.8947 | 0.9027 |
| 0.0081 | 0.3608 | 280 | 0.0182 | 0.9960 | 0.9153 | 0.9474 | 0.9310 |
| 0.0155 | 0.3866 | 300 | 0.0145 | 0.9980 | 0.9818 | 0.9474 | 0.9643 |
| 0.0075 | 0.4124 | 320 | 0.0165 | 0.9975 | 0.9643 | 0.9474 | 0.9558 |
| 0.0033 | 0.4381 | 340 | 0.0139 | 0.9975 | 0.9643 | 0.9474 | 0.9558 |
| 0.01 | 0.4639 | 360 | 0.0136 | 0.9970 | 0.9474 | 0.9474 | 0.9474 |
| 0.0018 | 0.4897 | 380 | 0.0146 | 0.9970 | 0.9474 | 0.9474 | 0.9474 |
| 0.0006 | 0.5155 | 400 | 0.0138 | 0.9975 | 0.9815 | 0.9298 | 0.9550 |
| 0.003 | 0.5412 | 420 | 0.0135 | 0.9965 | 0.9310 | 0.9474 | 0.9391 |
| 0.0035 | 0.5670 | 440 | 0.0141 | 0.9965 | 0.9808 | 0.8947 | 0.9358 |
| 0.0024 | 0.5928 | 460 | 0.0148 | 0.9965 | 0.9808 | 0.8947 | 0.9358 |
| 0.0203 | 0.6186 | 480 | 0.0136 | 0.9970 | 0.9474 | 0.9474 | 0.9474 |
| 0.0293 | 0.6443 | 500 | 0.0164 | 0.9970 | 0.9811 | 0.9123 | 0.9455 |
| 0.0078 | 0.6701 | 520 | 0.0149 | 0.9970 | 0.9474 | 0.9474 | 0.9474 |
| 0.0291 | 0.6959 | 540 | 0.0147 | 0.9975 | 0.9643 | 0.9474 | 0.9558 |
| 0.0119 | 0.7216 | 560 | 0.0136 | 0.9970 | 0.9474 | 0.9474 | 0.9474 |
| 0.002 | 0.7474 | 580 | 0.0138 | 0.9980 | 0.9818 | 0.9474 | 0.9643 |
| 0.0009 | 0.7732 | 600 | 0.0140 | 0.9980 | 0.9818 | 0.9474 | 0.9643 |
| 0.0022 | 0.7990 | 620 | 0.0134 | 0.9980 | 0.9818 | 0.9474 | 0.9643 |
| 0.0149 | 0.8247 | 640 | 0.0136 | 0.9980 | 0.9818 | 0.9474 | 0.9643 |
| 0.0397 | 0.8505 | 660 | 0.0140 | 0.9980 | 0.9818 | 0.9474 | 0.9643 |
| 0.0058 | 0.8763 | 680 | 0.0135 | 0.9980 | 0.9818 | 0.9474 | 0.9643 |
| 0.0153 | 0.9021 | 700 | 0.0132 | 0.9980 | 0.9818 | 0.9474 | 0.9643 |
| 0.0122 | 0.9278 | 720 | 0.0132 | 0.9980 | 0.9818 | 0.9474 | 0.9643 |
| 0.0276 | 0.9536 | 740 | 0.0132 | 0.9980 | 0.9818 | 0.9474 | 0.9643 |
| 0.0042 | 0.9794 | 760 | 0.0130 | 0.9980 | 0.9818 | 0.9474 | 0.9643 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |