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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: v1_5_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. -->
# v1_5_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: 0.3143
- Accuracy: 0.8639
- Precision: 0.7383
- Recall: 0.7453
- F1: 0.7418
## 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 |
| 0.6808 | 0.0575 | 20 | 0.5704 | 0.7401 | 0.5385 | 0.0660 | 0.1176 |
| 0.4764 | 0.1149 | 40 | 0.4768 | 0.7574 | 0.5303 | 0.6604 | 0.5882 |
| 0.4099 | 0.1724 | 60 | 0.4052 | 0.8020 | 0.6275 | 0.6038 | 0.6154 |
| 0.346 | 0.2299 | 80 | 0.3761 | 0.8366 | 0.6961 | 0.6698 | 0.6827 |
| 0.2929 | 0.2874 | 100 | 0.3664 | 0.8366 | 0.6887 | 0.6887 | 0.6887 |
| 0.3822 | 0.3448 | 120 | 0.3551 | 0.8515 | 0.6983 | 0.7642 | 0.7297 |
| 0.3955 | 0.4023 | 140 | 0.3442 | 0.8589 | 0.7634 | 0.6698 | 0.7136 |
| 0.34 | 0.4598 | 160 | 0.3399 | 0.8614 | 0.7232 | 0.7642 | 0.7431 |
| 0.2897 | 0.5172 | 180 | 0.3244 | 0.8614 | 0.7404 | 0.7264 | 0.7333 |
| 0.2599 | 0.5747 | 200 | 0.3225 | 0.8639 | 0.7383 | 0.7453 | 0.7418 |
| 0.34 | 0.6322 | 220 | 0.3178 | 0.8688 | 0.7573 | 0.7358 | 0.7464 |
| 0.2969 | 0.6897 | 240 | 0.3178 | 0.8564 | 0.7222 | 0.7358 | 0.7290 |
| 0.3179 | 0.7471 | 260 | 0.3128 | 0.8663 | 0.7453 | 0.7453 | 0.7453 |
| 0.2901 | 0.8046 | 280 | 0.3146 | 0.8639 | 0.7383 | 0.7453 | 0.7418 |
| 0.2587 | 0.8621 | 300 | 0.3138 | 0.8639 | 0.7383 | 0.7453 | 0.7418 |
| 0.326 | 0.9195 | 320 | 0.3151 | 0.8589 | 0.7248 | 0.7453 | 0.7349 |
| 0.3475 | 0.9770 | 340 | 0.3143 | 0.8639 | 0.7383 | 0.7453 | 0.7418 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |