---
library_name: peft
base_model: peiyi9979/math-shepherd-mistral-7b-prm
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: v3c_mistral_lora_lastn
  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. -->

# v3c_mistral_lora_lastn

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: 0.3067
- Accuracy: 0.8592
- Precision: 0.8580
- Recall: 0.5968
- F1: 0.7040

## 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: 4
- total_train_batch_size: 128
- 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    | 0.6026          | 0.7339   | 0.6       | 0.1542 | 0.2453 |
| 0.5522        | 0.0495 | 20   | 0.5768          | 0.7395   | 0.5682    | 0.2964 | 0.3896 |
| 0.4818        | 0.0990 | 40   | 0.4859          | 0.7761   | 0.6835    | 0.3755 | 0.4847 |
| 0.3892        | 0.1485 | 60   | 0.4218          | 0.7982   | 0.6766    | 0.5375 | 0.5991 |
| 0.2916        | 0.1980 | 80   | 0.3747          | 0.8237   | 0.7701    | 0.5296 | 0.6276 |
| 0.2191        | 0.2475 | 100  | 0.3538          | 0.8304   | 0.7778    | 0.5534 | 0.6467 |
| 0.2189        | 0.2970 | 120  | 0.3754          | 0.8248   | 0.88      | 0.4348 | 0.5820 |
| 0.1841        | 0.3465 | 140  | 0.3427          | 0.8415   | 0.8438    | 0.5336 | 0.6538 |
| 0.2144        | 0.3960 | 160  | 0.3301          | 0.8404   | 0.8303    | 0.5415 | 0.6555 |
| 0.2638        | 0.4455 | 180  | 0.3202          | 0.8470   | 0.8485    | 0.5534 | 0.6699 |
| 0.2032        | 0.4950 | 200  | 0.3125          | 0.8570   | 0.8370    | 0.6087 | 0.7048 |
| 0.1703        | 0.5446 | 220  | 0.3295          | 0.8337   | 0.8552    | 0.4901 | 0.6231 |
| 0.175         | 0.5941 | 240  | 0.3116          | 0.8503   | 0.8471    | 0.5692 | 0.6809 |
| 0.1927        | 0.6436 | 260  | 0.3218          | 0.8459   | 0.8654    | 0.5336 | 0.6601 |
| 0.1848        | 0.6931 | 280  | 0.3069          | 0.8647   | 0.8659    | 0.6126 | 0.7176 |
| 0.222         | 0.7426 | 300  | 0.3036          | 0.8581   | 0.8613    | 0.5889 | 0.6995 |
| 0.1693        | 0.7921 | 320  | 0.3096          | 0.8525   | 0.8614    | 0.5652 | 0.6826 |
| 0.1752        | 0.8416 | 340  | 0.3108          | 0.8503   | 0.8554    | 0.5613 | 0.6778 |
| 0.2353        | 0.8911 | 360  | 0.3072          | 0.8592   | 0.8580    | 0.5968 | 0.7040 |
| 0.1984        | 0.9406 | 380  | 0.3078          | 0.8603   | 0.8629    | 0.5968 | 0.7056 |
| 0.2194        | 0.9901 | 400  | 0.3067          | 0.8592   | 0.8580    | 0.5968 | 0.7040 |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3