---
library_name: transformers
base_model: peiyi9979/math-shepherd-mistral-7b-prm
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: v1_5_mistral_full_1122
  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. -->

# v1_5_mistral_full_1122

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: 0.2520
- Accuracy: 0.9035
- Precision: 0.8317
- Recall: 0.7925
- F1: 0.8116

## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 765837
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Use 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     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3603        | 0.0575 | 20   | 0.4322          | 0.7550   | 0.5189    | 0.9057 | 0.6598 |
| 0.3252        | 0.1151 | 40   | 0.3813          | 0.8663   | 0.825     | 0.6226 | 0.7097 |
| 0.3574        | 0.1726 | 60   | 0.3824          | 0.8045   | 0.5789    | 0.9340 | 0.7148 |
| 0.31          | 0.2301 | 80   | 0.3275          | 0.8614   | 0.7155    | 0.7830 | 0.7477 |
| 0.2803        | 0.2877 | 100  | 0.3611          | 0.8738   | 0.7723    | 0.7358 | 0.7536 |
| 0.3893        | 0.3452 | 120  | 0.3245          | 0.8416   | 0.6694    | 0.7830 | 0.7217 |
| 0.3624        | 0.4027 | 140  | 0.3172          | 0.8812   | 0.8372    | 0.6792 | 0.75   |
| 0.3081        | 0.4603 | 160  | 0.3283          | 0.8639   | 0.7742    | 0.6792 | 0.7236 |
| 0.242         | 0.5178 | 180  | 0.2907          | 0.8837   | 0.7658    | 0.8019 | 0.7834 |
| 0.2692        | 0.5753 | 200  | 0.2787          | 0.8911   | 0.7925    | 0.7925 | 0.7925 |
| 0.2866        | 0.6329 | 220  | 0.2675          | 0.8787   | 0.7478    | 0.8113 | 0.7783 |
| 0.27          | 0.6904 | 240  | 0.2702          | 0.9035   | 0.8317    | 0.7925 | 0.8116 |
| 0.3112        | 0.7479 | 260  | 0.2605          | 0.9059   | 0.8864    | 0.7358 | 0.8041 |
| 0.2032        | 0.8055 | 280  | 0.2700          | 0.9010   | 0.8587    | 0.7453 | 0.7980 |
| 0.2326        | 0.8630 | 300  | 0.2549          | 0.9059   | 0.8333    | 0.8019 | 0.8173 |
| 0.2714        | 0.9205 | 320  | 0.2511          | 0.9035   | 0.8317    | 0.7925 | 0.8116 |
| 0.2562        | 0.9781 | 340  | 0.2520          | 0.9035   | 0.8317    | 0.7925 | 0.8116 |


### Framework versions

- Transformers 4.46.0
- Pytorch 2.4.0+cu118
- Datasets 3.0.0
- Tokenizers 0.20.1