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---
license: apache-2.0
library_name: peft
tags:
- unsloth
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.3
model-index:
- name: mistral_7b_v_MetaMathQA_40K_reverse
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. -->
# mistral_7b_v_MetaMathQA_40K_reverse
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co./mistralai/Mistral-7B-v0.3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4421
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.02
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7252 | 0.0211 | 13 | 0.6225 |
| 0.5814 | 0.0421 | 26 | 0.5900 |
| 0.5599 | 0.0632 | 39 | 0.5751 |
| 0.548 | 0.0842 | 52 | 0.5708 |
| 0.5371 | 0.1053 | 65 | 0.5625 |
| 0.5347 | 0.1264 | 78 | 0.5600 |
| 0.5232 | 0.1474 | 91 | 0.5529 |
| 0.5382 | 0.1685 | 104 | 0.5478 |
| 0.5178 | 0.1896 | 117 | 0.5482 |
| 0.5272 | 0.2106 | 130 | 0.5423 |
| 0.5135 | 0.2317 | 143 | 0.5397 |
| 0.4943 | 0.2527 | 156 | 0.5321 |
| 0.5012 | 0.2738 | 169 | 0.5323 |
| 0.5077 | 0.2949 | 182 | 0.5300 |
| 0.5031 | 0.3159 | 195 | 0.5233 |
| 0.506 | 0.3370 | 208 | 0.5238 |
| 0.4851 | 0.3580 | 221 | 0.5180 |
| 0.4915 | 0.3791 | 234 | 0.5146 |
| 0.4826 | 0.4002 | 247 | 0.5150 |
| 0.4964 | 0.4212 | 260 | 0.5096 |
| 0.4989 | 0.4423 | 273 | 0.5050 |
| 0.4846 | 0.4633 | 286 | 0.5021 |
| 0.4776 | 0.4844 | 299 | 0.5006 |
| 0.4725 | 0.5055 | 312 | 0.4927 |
| 0.4752 | 0.5265 | 325 | 0.4898 |
| 0.4719 | 0.5476 | 338 | 0.4862 |
| 0.4689 | 0.5687 | 351 | 0.4817 |
| 0.4573 | 0.5897 | 364 | 0.4772 |
| 0.4536 | 0.6108 | 377 | 0.4754 |
| 0.4536 | 0.6318 | 390 | 0.4700 |
| 0.4519 | 0.6529 | 403 | 0.4664 |
| 0.4448 | 0.6740 | 416 | 0.4633 |
| 0.4327 | 0.6950 | 429 | 0.4618 |
| 0.4528 | 0.7161 | 442 | 0.4586 |
| 0.4379 | 0.7371 | 455 | 0.4557 |
| 0.4504 | 0.7582 | 468 | 0.4537 |
| 0.4436 | 0.7793 | 481 | 0.4525 |
| 0.4451 | 0.8003 | 494 | 0.4497 |
| 0.435 | 0.8214 | 507 | 0.4482 |
| 0.4247 | 0.8424 | 520 | 0.4466 |
| 0.4295 | 0.8635 | 533 | 0.4455 |
| 0.4204 | 0.8846 | 546 | 0.4444 |
| 0.4381 | 0.9056 | 559 | 0.4433 |
| 0.4355 | 0.9267 | 572 | 0.4430 |
| 0.4234 | 0.9478 | 585 | 0.4424 |
| 0.4261 | 0.9688 | 598 | 0.4421 |
| 0.4266 | 0.9899 | 611 | 0.4421 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |