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---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.2
model-index:
- name: mistral-7b-scientific-mcq
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-scientific-mcq
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7480
## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9911 | 0.0581 | 100 | 0.8124 |
| 0.879 | 0.1162 | 200 | 0.7703 |
| 0.9359 | 0.1743 | 300 | 0.7576 |
| 0.7608 | 0.2325 | 400 | 0.7523 |
| 0.8144 | 0.2906 | 500 | 0.7469 |
| 0.8655 | 0.3487 | 600 | 0.7435 |
| 0.6748 | 0.4068 | 700 | 0.7390 |
| 0.7004 | 0.4649 | 800 | 0.7369 |
| 0.7561 | 0.5230 | 900 | 0.7351 |
| 0.7053 | 0.5811 | 1000 | 0.7317 |
| 0.7122 | 0.6393 | 1100 | 0.7294 |
| 0.7431 | 0.6974 | 1200 | 0.7279 |
| 0.6102 | 0.7555 | 1300 | 0.7255 |
| 0.7041 | 0.8136 | 1400 | 0.7244 |
| 0.7339 | 0.8717 | 1500 | 0.7227 |
| 0.6648 | 0.9298 | 1600 | 0.7207 |
| 0.5682 | 0.9879 | 1700 | 0.7192 |
| 0.6745 | 1.0461 | 1800 | 0.7242 |
| 0.6003 | 1.1042 | 1900 | 0.7258 |
| 0.6755 | 1.1623 | 2000 | 0.7273 |
| 0.6815 | 1.2204 | 2100 | 0.7265 |
| 0.5531 | 1.2785 | 2200 | 0.7253 |
| 0.5 | 1.3366 | 2300 | 0.7250 |
| 0.666 | 1.3947 | 2400 | 0.7236 |
| 0.518 | 1.4529 | 2500 | 0.7247 |
| 0.6223 | 1.5110 | 2600 | 0.7240 |
| 0.565 | 1.5691 | 2700 | 0.7234 |
| 0.5541 | 1.6272 | 2800 | 0.7220 |
| 0.7622 | 1.6853 | 2900 | 0.7220 |
| 0.5212 | 1.7434 | 3000 | 0.7223 |
| 0.6089 | 1.8015 | 3100 | 0.7205 |
| 0.6908 | 1.8597 | 3200 | 0.7210 |
| 0.6138 | 1.9178 | 3300 | 0.7204 |
| 0.6425 | 1.9759 | 3400 | 0.7199 |
| 0.4918 | 2.0340 | 3500 | 0.7416 |
| 0.5432 | 2.0921 | 3600 | 0.7468 |
| 0.6497 | 2.1502 | 3700 | 0.7463 |
| 0.5068 | 2.2083 | 3800 | 0.7448 |
| 0.5502 | 2.2665 | 3900 | 0.7475 |
| 0.4795 | 2.3246 | 4000 | 0.7482 |
| 0.5718 | 2.3827 | 4100 | 0.7486 |
| 0.5154 | 2.4408 | 4200 | 0.7474 |
| 0.6959 | 2.4989 | 4300 | 0.7479 |
| 0.5848 | 2.5570 | 4400 | 0.7473 |
| 0.5662 | 2.6151 | 4500 | 0.7479 |
| 0.4357 | 2.6733 | 4600 | 0.7482 |
| 0.5318 | 2.7314 | 4700 | 0.7476 |
| 0.4631 | 2.7895 | 4800 | 0.7480 |
| 0.5852 | 2.8476 | 4900 | 0.7481 |
| 0.5633 | 2.9057 | 5000 | 0.7480 |
| 0.5831 | 2.9638 | 5100 | 0.7480 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1 |