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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_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 |