---
library_name: peft
base_model: peiyi9979/math-shepherd-mistral-7b-prm
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: v1_5_mistral_lora32
  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_lora32

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: 0.3153
- Accuracy: 0.8614
- Precision: 0.7273
- Recall: 0.7547
- F1: 0.7407

## 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    | 0.5958          | 0.7376   | 0.5       | 0.0660 | 0.1167 |
| 0.6819        | 0.0575 | 20   | 0.5694          | 0.7450   | 0.6154    | 0.0755 | 0.1345 |
| 0.4789        | 0.1149 | 40   | 0.4776          | 0.7550   | 0.5263    | 0.6604 | 0.5858 |
| 0.4108        | 0.1724 | 60   | 0.4043          | 0.8045   | 0.6286    | 0.6226 | 0.6256 |
| 0.3486        | 0.2299 | 80   | 0.3752          | 0.8366   | 0.6961    | 0.6698 | 0.6827 |
| 0.2936        | 0.2874 | 100  | 0.3650          | 0.8465   | 0.7115    | 0.6981 | 0.7048 |
| 0.3788        | 0.3448 | 120  | 0.3579          | 0.8465   | 0.6864    | 0.7642 | 0.7232 |
| 0.3918        | 0.4023 | 140  | 0.3483          | 0.8564   | 0.7449    | 0.6887 | 0.7157 |
| 0.3386        | 0.4598 | 160  | 0.3409          | 0.8614   | 0.7193    | 0.7736 | 0.7455 |
| 0.2864        | 0.5172 | 180  | 0.3257          | 0.8663   | 0.75      | 0.7358 | 0.7429 |
| 0.2581        | 0.5747 | 200  | 0.3223          | 0.8663   | 0.7407    | 0.7547 | 0.7477 |
| 0.3373        | 0.6322 | 220  | 0.3174          | 0.8663   | 0.75      | 0.7358 | 0.7429 |
| 0.3006        | 0.6897 | 240  | 0.3172          | 0.8564   | 0.7222    | 0.7358 | 0.7290 |
| 0.3157        | 0.7471 | 260  | 0.3143          | 0.8639   | 0.7339    | 0.7547 | 0.7442 |
| 0.291         | 0.8046 | 280  | 0.3137          | 0.8639   | 0.7339    | 0.7547 | 0.7442 |
| 0.2578        | 0.8621 | 300  | 0.3168          | 0.8639   | 0.7339    | 0.7547 | 0.7442 |
| 0.3223        | 0.9195 | 320  | 0.3157          | 0.8614   | 0.7273    | 0.7547 | 0.7407 |
| 0.3448        | 0.9770 | 340  | 0.3153          | 0.8614   | 0.7273    | 0.7547 | 0.7407 |


### Framework versions

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