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---
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 |