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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: 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 |