|
--- |
|
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_base_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_base_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: 0.0134 |
|
- Accuracy: 0.9975 |
|
- Precision: 0.9643 |
|
- Recall: 0.9474 |
|
- F1: 0.9558 |
|
|
|
## 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 | |
|
| 0.4498 | 0.0258 | 20 | 0.2868 | 0.9474 | 0.1449 | 0.1754 | 0.1587 | |
|
| 0.242 | 0.0515 | 40 | 0.1434 | 0.9672 | 0.2 | 0.0526 | 0.0833 | |
|
| 0.1628 | 0.0773 | 60 | 0.1080 | 0.9692 | 0.3810 | 0.1404 | 0.2051 | |
|
| 0.1241 | 0.1031 | 80 | 0.0874 | 0.9707 | 0.475 | 0.3333 | 0.3918 | |
|
| 0.0676 | 0.1289 | 100 | 0.0690 | 0.9692 | 0.4713 | 0.7193 | 0.5694 | |
|
| 0.03 | 0.1546 | 120 | 0.0472 | 0.9821 | 0.6296 | 0.8947 | 0.7391 | |
|
| 0.0109 | 0.1804 | 140 | 0.0341 | 0.9911 | 0.8305 | 0.8596 | 0.8448 | |
|
| 0.043 | 0.2062 | 160 | 0.0337 | 0.9916 | 0.8333 | 0.8772 | 0.8547 | |
|
| 0.0233 | 0.2320 | 180 | 0.0272 | 0.9926 | 0.8281 | 0.9298 | 0.8760 | |
|
| 0.029 | 0.2577 | 200 | 0.0233 | 0.9921 | 0.8254 | 0.9123 | 0.8667 | |
|
| 0.0138 | 0.2835 | 220 | 0.0210 | 0.9930 | 0.8413 | 0.9298 | 0.8833 | |
|
| 0.0141 | 0.3093 | 240 | 0.0175 | 0.9955 | 0.9286 | 0.9123 | 0.9204 | |
|
| 0.0037 | 0.3351 | 260 | 0.0170 | 0.9940 | 0.8814 | 0.9123 | 0.8966 | |
|
| 0.0076 | 0.3608 | 280 | 0.0186 | 0.9955 | 0.9 | 0.9474 | 0.9231 | |
|
| 0.0133 | 0.3866 | 300 | 0.0152 | 0.9975 | 0.9643 | 0.9474 | 0.9558 | |
|
| 0.0084 | 0.4124 | 320 | 0.0164 | 0.9970 | 0.9474 | 0.9474 | 0.9474 | |
|
| 0.0039 | 0.4381 | 340 | 0.0141 | 0.9975 | 0.9643 | 0.9474 | 0.9558 | |
|
| 0.0102 | 0.4639 | 360 | 0.0138 | 0.9970 | 0.9474 | 0.9474 | 0.9474 | |
|
| 0.0019 | 0.4897 | 380 | 0.0152 | 0.9965 | 0.9310 | 0.9474 | 0.9391 | |
|
| 0.0007 | 0.5155 | 400 | 0.0145 | 0.9970 | 0.9636 | 0.9298 | 0.9464 | |
|
| 0.0028 | 0.5412 | 420 | 0.0141 | 0.9965 | 0.9310 | 0.9474 | 0.9391 | |
|
| 0.0035 | 0.5670 | 440 | 0.0147 | 0.9960 | 0.9623 | 0.8947 | 0.9273 | |
|
| 0.0016 | 0.5928 | 460 | 0.0159 | 0.9965 | 0.9808 | 0.8947 | 0.9358 | |
|
| 0.0262 | 0.6186 | 480 | 0.0141 | 0.9970 | 0.9474 | 0.9474 | 0.9474 | |
|
| 0.0294 | 0.6443 | 500 | 0.0165 | 0.9970 | 0.9811 | 0.9123 | 0.9455 | |
|
| 0.0054 | 0.6701 | 520 | 0.0145 | 0.9970 | 0.9474 | 0.9474 | 0.9474 | |
|
| 0.0293 | 0.6959 | 540 | 0.0148 | 0.9970 | 0.9474 | 0.9474 | 0.9474 | |
|
| 0.0133 | 0.7216 | 560 | 0.0137 | 0.9970 | 0.9474 | 0.9474 | 0.9474 | |
|
| 0.0028 | 0.7474 | 580 | 0.0141 | 0.9980 | 0.9818 | 0.9474 | 0.9643 | |
|
| 0.0012 | 0.7732 | 600 | 0.0142 | 0.9980 | 0.9818 | 0.9474 | 0.9643 | |
|
| 0.0018 | 0.7990 | 620 | 0.0136 | 0.9975 | 0.9643 | 0.9474 | 0.9558 | |
|
| 0.0164 | 0.8247 | 640 | 0.0140 | 0.9975 | 0.9643 | 0.9474 | 0.9558 | |
|
| 0.0359 | 0.8505 | 660 | 0.0143 | 0.9980 | 0.9818 | 0.9474 | 0.9643 | |
|
| 0.0038 | 0.8763 | 680 | 0.0137 | 0.9980 | 0.9818 | 0.9474 | 0.9643 | |
|
| 0.011 | 0.9021 | 700 | 0.0134 | 0.9975 | 0.9643 | 0.9474 | 0.9558 | |
|
| 0.0144 | 0.9278 | 720 | 0.0134 | 0.9975 | 0.9643 | 0.9474 | 0.9558 | |
|
| 0.0284 | 0.9536 | 740 | 0.0134 | 0.9980 | 0.9818 | 0.9474 | 0.9643 | |
|
| 0.0066 | 0.9794 | 760 | 0.0134 | 0.9975 | 0.9643 | 0.9474 | 0.9558 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.13.2 |
|
- Transformers 4.46.0 |
|
- Pytorch 2.5.1+cu124 |
|
- Datasets 3.1.0 |
|
- Tokenizers 0.20.3 |
