---
library_name: peft
base_model: peiyi9979/math-shepherd-mistral-7b-prm
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: v3d_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. -->

# v3d_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: 0.3848
- Accuracy: 0.8329
- Precision: 0.7398
- Recall: 0.4527
- F1: 0.5617

## 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    | 0.6112          | 0.7506   | 0.3659    | 0.0746 | 0.1240 |
| 0.5265        | 0.0532 | 20   | 0.6081          | 0.7647   | 0.5455    | 0.0299 | 0.0566 |
| 0.4127        | 0.1064 | 40   | 0.5412          | 0.7682   | 0.7       | 0.0348 | 0.0664 |
| 0.3347        | 0.1596 | 60   | 0.5020          | 0.7741   | 0.7368    | 0.0697 | 0.1273 |
| 0.3077        | 0.2128 | 80   | 0.4463          | 0.7965   | 0.6944    | 0.2488 | 0.3663 |
| 0.307         | 0.2660 | 100  | 0.4498          | 0.8      | 0.6782    | 0.2935 | 0.4097 |
| 0.2413        | 0.3191 | 120  | 0.4316          | 0.8141   | 0.7087    | 0.3632 | 0.4803 |
| 0.326         | 0.3723 | 140  | 0.4107          | 0.8235   | 0.7257    | 0.4080 | 0.5223 |
| 0.2404        | 0.4255 | 160  | 0.4615          | 0.8094   | 0.7671    | 0.2786 | 0.4088 |
| 0.2962        | 0.4787 | 180  | 0.4205          | 0.8282   | 0.7619    | 0.3980 | 0.5229 |
| 0.2727        | 0.5319 | 200  | 0.4830          | 0.8      | 0.7627    | 0.2239 | 0.3462 |
| 0.2844        | 0.5851 | 220  | 0.4187          | 0.8259   | 0.7524    | 0.3930 | 0.5163 |
| 0.2632        | 0.6383 | 240  | 0.4037          | 0.8235   | 0.7339    | 0.3980 | 0.5161 |
| 0.2499        | 0.6915 | 260  | 0.3885          | 0.8247   | 0.7241    | 0.4179 | 0.5300 |
| 0.2121        | 0.7447 | 280  | 0.3953          | 0.8224   | 0.7232    | 0.4030 | 0.5176 |
| 0.2704        | 0.7979 | 300  | 0.3849          | 0.8329   | 0.736     | 0.4577 | 0.5644 |
| 0.2333        | 0.8511 | 320  | 0.3878          | 0.8318   | 0.7417    | 0.4428 | 0.5545 |
| 0.2896        | 0.9043 | 340  | 0.3886          | 0.8306   | 0.7395    | 0.4378 | 0.55   |
| 0.2398        | 0.9574 | 360  | 0.3848          | 0.8329   | 0.7398    | 0.4527 | 0.5617 |


### Framework versions

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